Denis Maua Webpage

Automated Essay Scoring for Brazilian Portuguese - Evidence from Cross-Prompt Evaluation of ENEM Essays

Wed, 01 Apr 2026 00:00:00 +0000

Automated Reformulation of Argumentative Essays to Improve Argument Organization and Development

Wed, 01 Apr 2026 00:00:00 +0000

Evaluating Automated Scoring Models on Official ENEM Essays

Wed, 01 Apr 2026 00:00:00 +0000

Neuro-symbolic Approaches for Rubric-Based Automatic Essay Evaluation of ENEM Essays

Wed, 01 Apr 2026 00:00:00 +0000

Branch and Bound Search for Exact MAP Inference in Credal Networks

Thu, 01 Jan 2026 00:00:00 +0000

Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models

Thu, 01 Jan 2026 00:00:00 +0000

An Empirical Analysis of Large Language Models for Automated Cross-Prompt Essay Trait Scoring in Brazilian Portuguese

Wed, 01 Oct 2025 00:00:00 +0000

Dealing with cycles in graph-based probabilistic models: the case of Logical Credal Networks

Tue, 01 Jul 2025 00:00:00 +0000

Neuro Probabilistic Logic Programming

Fri, 09 May 2025 16:30:00 -0300

Investigating Universal Adversarial Attacks Against Transformers-Based Automatic Essay Scoring Systems

Wed, 01 Jan 2025 00:00:00 +0000

A Big Challenge: Tools to Guarantee Robust and Controlled Behavior of Large Language Models

Fri, 01 Nov 2024 00:00:00 +0000

dPASP: A Probabilistic Logic Programming Environment For Neurosymbolic Learning and Reasoning

Thu, 01 Aug 2024 00:00:00 +0000

Projects

Sun, 19 May 2024 00:00:00 +0000

A Compositional Atlas for Algebraic Circuits

Mon, 01 Jan 2024 00:00:00 +0000

A New Benchmark for Automatic Essay Scoring in Portuguese

Mon, 01 Jan 2024 00:00:00 +0000

Probabilistic Logic Programming under the L-Stable Semantics

Mon, 01 Jan 2024 00:00:00 +0000

🎉 Easily create your own simple yet highly customizable blog

Fri, 27 Oct 2023 00:00:00 +0000

Welcome 👋

Table of Contents

Overview

The Hugo Blox website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more

Get Started

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To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

As a token of appreciation for sponsoring, you can unlock awesome rewards and extra features 🦄✨

Ecosystem

: Automatically import publications from BibTeX

Inspiration

are building with this template.

Features

Page builder - Create anything with no-code and
Edit any type of content - Blog posts, publications, talks, slides, projects, and more!
Create content in , , or
Plugin System - Fully customizable
Display Code and Math - Code syntax highlighting and LaTeX math supported
Integrations - , , Maps, Contact Forms, and more!
Beautiful Site - Simple and refreshing one-page design
Industry-Leading SEO - Help get your website found on search engines and social media
Media Galleries - Display your images and videos with captions in a customizable gallery
Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site
Multi-language - 35+ language packs including English, 中文, and Português
Multi-user - Each author gets their own profile page
Privacy Pack - Assists with GDPR
Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects
One-Click Deployment - No servers. No databases. Only files.

Themes

Hugo Blox and its templates come with automatic day (light) and night (dark) mode built-in. Visitors can choose their preferred mode by clicking the sun/moon icon in the header.

for your site. Themes are fully customizable.

License

Released under the license.

🧠 Sharpen your thinking with a second brain

Thu, 26 Oct 2023 00:00:00 +0000

Create a personal knowledge base and share your knowledge with your peers.

Hugo Blox web framework empowers you with one of the most flexible note-taking capabilities out there.

Create a powerful knowledge base that works on top of a local folder of plain text Markdown files.

Use it as your second brain, either publicly sharing your knowledge with your peers via your website, or via a private GitHub repository and password-protected site just for yourself.

Mindmaps

Hugo Blox supports a Markdown extension for mindmaps.

With this open format, can even edit your mindmaps in other popular tools such as Obsidian.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
  - Hugo Blox
  - blox-plugins-netlify
  - blox-plugins-netlify-cms
  - blox-plugins-reveal
```

renders as

- Hugo Modules
  - Hugo Blox
  - blox-plugins-netlify
  - blox-plugins-netlify-cms
  - blox-plugins-reveal

Anh here’s a more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
  - Links
    - [Hugo Blox Docs](https://docs.hugoblox.com/)
    - [Discord Community](https://discord.gg/z8wNYzb)
    - [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
  - Features
    - Markdown formatting
    - **inline** ~~text~~ *styles*
    - multiline
      text
    - `inline code`
    -
      ```js
      console.log('hello');
      console.log('code block');
      ```
    - Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
  - Links
    - [Hugo Blox Docs](https://docs.hugoblox.com/)
    - [Discord Community](https://discord.gg/z8wNYzb)
    - [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
  - Features
    - Markdown formatting
    - **inline** ~~text~~ *styles*
    - multiline
      text
    - `inline code`
    -
      ```js
      console.log('hello');
      console.log('code block');
      ```
    - Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Highlighting

Highlight important text with mark:

<mark>Highlighted text</mark>

Callouts

Use (aka asides, hints, or alerts) to draw attention to notes, tips, and warnings.

By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.

{{% callout note %}}
A Markdown aside is useful for displaying notices, hints, or definitions to your readers.
{{% /callout %}}

renders as

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Or use the warning callout type so your readers don’t miss critical details:

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

📈 Communicate your results effectively with the best data visualizations

Wed, 25 Oct 2023 00:00:00 +0000

Hugo Blox is designed to give technical content creators a seamless experience. You can focus on the content and Hugo Blox handles the rest.

Use popular tools such as Plotly, Mermaid, and data frames.

Charts

Hugo Blox supports the popular format for interactive data visualizations. With Plotly, you can design almost any kind of visualization you can imagine!

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the useful.

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
    John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Data Frames

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

👩🏼‍🏫 Teach academic courses

Tue, 24 Oct 2023 00:00:00 +0000

is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Citation

Here’s an example of citing a publication using the cite shortcode:

You can also use the default view by omitting the view parameter:

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube D2vj0WcvH5c >}}

Bilibili:

{{< bilibili BV1WV4y1r7DF >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your , and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. Enable math by setting the math: true option in your page’s front matter, or enable math for your entire site by toggling math in your config/_default/params.yaml file:

features:
  math:
    enable: true

To render inline or block math, wrap your LaTeX math with $...$ or $$...$$, respectively.

Example math block:

$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math $\nabla F(\mathbf{x}_{n})$ renders as $\nabla F(\mathbf{x}_{n})$.

Example multi-line math using the math linebreak (\\):

$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

Experience

Tue, 24 Oct 2023 00:00:00 +0000

Learn JavaScript

Tue, 24 Oct 2023 00:00:00 +0000

is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube w7Ft2ymGmfc >}}

Bilibili:

{{< bilibili id="BV1WV4y1r7DF" >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your , and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively.

We wrap the LaTeX math in the Hugo Blox math shortcode to prevent Hugo rendering our math as Markdown.

Example math block:

{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}

renders as

Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$ .

Example multi-line math using the math linebreak (\\):

{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

Learn Python

Tue, 24 Oct 2023 00:00:00 +0000

is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube w7Ft2ymGmfc >}}

Bilibili:

{{< bilibili id="BV1WV4y1r7DF" >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your , and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively.

We wrap the LaTeX math in the Hugo Blox math shortcode to prevent Hugo rendering our math as Markdown.

Example math block:

{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}

renders as

Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$ .

Example multi-line math using the math linebreak (\\):

{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

✅ Manage your projects

Mon, 23 Oct 2023 00:00:00 +0000

Easily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!

Ideation

Hugo Blox supports a Markdown extension for mindmaps.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
  - Hugo Blox
  - blox-plugins-netlify
  - blox-plugins-netlify-cms
  - blox-plugins-reveal
```

renders as

- Hugo Modules
  - Hugo Blox
  - blox-plugins-netlify
  - blox-plugins-netlify-cms
  - blox-plugins-reveal

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done,    des1, 2014-01-06,2014-01-08
Active        :active,  des2, 2014-01-07, 3d
Parallel 1   :         des3, after des1, 1d
Parallel 2   :         des4, after des1, 1d
Parallel 3   :         des5, after des3, 1d
Parallel 4   :         des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
  - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

Specifying credal sets with probabilistic answer set programming

Sat, 01 Jul 2023 00:00:00 +0000

Neural Inductive Logic Programming

Wed, 01 Feb 2023 00:00:00 +0000

Funding

FAPESP PI 2022/02937-9 (2023-02-01 to 2028-01-31)
CNPq Productivity 305136/2022-4 (2023-03-01 to 2026-02-28)

Participants

Denis Deratani Mauá, Coordinator
Fabio G. Cozman, Associated Researcher
Igor Cataneo Silveira, PhD candidate, Researcher
Naomi de Moraes, PhD candidate, Researcher
Renato Lui Geh, Software Developer, Researcher
Jonas Gonçalves, MSc student, Researcher
Anahí Coimbra, MSc student, Researcher

Abstract

Deep learning techniques have shown impressive results in low-level cognitive task such as image and speech recognition as well as in high-level cognitive tasks such as question answering and stochastic planning. Yet, developing effective deep learning solutions is notoriously challenging as they require massive amounts of data and compute (often beyond limited budgets of typical users), are very sensitive to domain shifts, and produce undesirable output that undermine end-user trust in the system. Good old-fashioned AI techniques based on knowledge representation and symbol manipulation are data efficient, generalizable, and produce mostly verifiable behavior; however they scale poorly, require costly knowledge acquisition procedures and have difficulty in coping with noise and uncertainty that are ubiquitous in real settings. Neurosymbolic approaches have recently re-emerged as a means to take the best of both approaches and deliver systems that are expressive and scalable, yet interpretable, generalizable, data efficient and trustworthy. This document describes a five-year research proposal to study the further development of neurosymbolic approaches based on logic programming, procedural probabilistic programming and combinations of both. Ultimately, this research seeks to advance the state-of-the-art of learning-based agents that go beyond the dominant view of learning as an optimization task in a continuous space guided by input-output examples. To this end, we shall extend current neurosymbolic logic programming systems with more expressive constructs and more efficient learning techniques, and evaluate them in challenging cognitive tasks such as text-based question answering and argumentation.

Outcomes

Software:

The dPASP system:
Documentation:
Tutorial:

Publications:

Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Tasks

Sun, 01 Jan 2023 00:00:00 +0000

On Modal Clustering with Gaussian Sum-Product Networks

Sun, 01 Jan 2023 00:00:00 +0000

Differentiable Planning for Optimal Liquidation

Sat, 01 Jan 2022 00:00:00 +0000

Differentiable Planning with Indefinite Horizon

Sat, 01 Jan 2022 00:00:00 +0000

Distinct Outcomes in COVID-19 Patients with Positive or Negative RT-PCR Test

Sat, 01 Jan 2022 00:00:00 +0000

Exploration Versus Exploitation in Model-Based Reinforcement Learning: An Empirical Study

Sat, 01 Jan 2022 00:00:00 +0000

Integrating Question Answering and Text-to-SQL in Portuguese

Sat, 01 Jan 2022 00:00:00 +0000

Time Robust Trees: Using Temporal Invariance to Improve Generalization

Sat, 01 Jan 2022 00:00:00 +0000

Timing to Intubation COVID-19 Patients: Can We Put It Off until Tomorrow?

Sat, 01 Jan 2022 00:00:00 +0000

Tractable Classification with Non-Ignorable Missing Data Using Generative Random Forests

Sat, 01 Jan 2022 00:00:00 +0000

Tractable Mode-Finding in Sum-Product Networks with Gaussian Leaves

Sat, 01 Jan 2022 00:00:00 +0000

Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging

Thu, 01 Jul 2021 00:00:00 +0000

Cautious Classification with Data Missing Not at Random using Generative Random Forests

Fri, 01 Jan 2021 00:00:00 +0000

Efficient algorithms for Risk-Sensitive Markov Decision Processes with limited budget

Fri, 01 Jan 2021 00:00:00 +0000

Fast And Accurate Learning of Probabilistic Circuits by Random Projections

Fri, 01 Jan 2021 00:00:00 +0000

Credal Networks: Specification, Algorithms, Complexity

Tue, 08 Dec 2020 11:00:00 +0000

Predictive Uncertainty Estimators for Sum-Product Networks

Sun, 01 Mar 2020 00:00:00 +0000

Funding Agency

CNPq grant 304012/2019-0 (ended)

Coordinator

Denis Deratani Mauá

Participants

Denis Deratani Mauá
Julissa G. Villanueva, PhD candidate at IME-USP
Renato Lui Geh, MSc student at IME-USP
Decio L. Soares, MSc student at IME-USP
Jonas Gonçalves, Undergraduate student at IME-USP

Abstract

Deep models have recently obtained impressive results in a wide range of tasks and domains, from computer vision to image and text processing to diagnosis and automated reasoning. Sum-product networks are special type of neural networks targeted at the representation of high-dimensionality probability distributions. Notably, the structure of a sum-product network (i.e., its graph) encodes a number of probabilistic independence assertions. In addition, each node of the network computes a valid distribution over its part of the variables. Such probabilistic semantics facilitates debugging, allows a wide range of queries and data (including missing and noisy data) to be handled properly and efficiently, and enables efficient structure learning algorithms.

In many applications, it is desirable to have not only a prediction (e.g., the probability of observing a certain object, say an image), but a confidence measure about its prediction. As sum-product networks are learned from data, the probabilities they calculate can be overly confident or too sensitive to hyperparameters on regions where data was scarce, conflicting or too noisy. In this research project, we plan on following two possible approaches to estimating the uncertainty of a sum-product network prediction. The first approach estimates uncertainty by analyzing the effect that small perturbations of the data have on the prediction. The second approach considers the variability of predictions among different networks (learned from the same data).

This research investigates predictive uncertainty estimators for sum-product networks.

A Contact Network-Based Approach for Online Planning of Containment Measures for COVID-19

Wed, 01 Jan 2020 00:00:00 +0000

Complexity results for probabilistic answer set programming

Wed, 01 Jan 2020 00:00:00 +0000

Decision-Aware Model Learning for Actor-Critic Methods: When Theory Does Not Meet Practice

Wed, 01 Jan 2020 00:00:00 +0000

Efficient Algorithms for Robustness Analysis of Maximum A Posteriori Inference in Selective Sum-Product Networks

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Finding Feasible Policies for Extreme Risk-Averse Agents in Probabilistic Planning

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Learning Probabilistic Sentential Decision Diagrams by Sampling

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On the Performance of Planning Through Backpropagation

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Prediction of Environmental Conditions for Maritime Navigation using a Network of Sensors: A Practical Application of Graph Neural Networks

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The Joy of Probabilistic Answer Set Programming: Semantics, complexity, expressivity, inference

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Thirty years of credal networks: Specification, algorithms and complexity

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Tractable inference in credal sentential decision diagrams

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Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks

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Deep Reactive Policies for Planning in Stochastic Nonlinear Domains

Tue, 01 Jan 2019 00:00:00 +0000

End-To-End Imitation Learning of Lane Following Policies Using Sum-Product Networks

Tue, 01 Jan 2019 00:00:00 +0000

Exploring the Space of Probabilistic Sentential Decision Diagrams

Tue, 01 Jan 2019 00:00:00 +0000

Robust Analysis of MAP Inference in Selective Sum-Product Networks

Tue, 01 Jan 2019 00:00:00 +0000

Speeding up parameter and rule learning for acyclic probabilistic logic programs

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The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws

Tue, 01 Jan 2019 00:00:00 +0000

The ENEM Challenge

Mon, 01 Oct 2018 00:00:00 +0000

It is widely accepted that university entrance exams require human-level intelligence to be satisfactorily solved. In particular, such exams require skills such as text and image understanding, informational retrieval, commonsense reasoning, and mathematical thinking.

Thus, university entrance exams constitute a good test for real Artificial Intelligence techniques.

The is an advanced High-School level exam widely applied every year by the Brazilian government to students that wish to undertake a University degree.

The ENEM Challenge consists in designing an autonomous system that matches the performance of a human students on the exam. The overall goal is to foster and evaluate the development of Artificial Intelligence techniques that have good performance on complex cognitive tasks, not particularly designed for AI systems. In addition, this challenge aims to promote and give more visiblity to the development of NLP tools for Brazilian Portuguese.

The informal and witty aim of the challenge is to get a digital student to be accepted at a main Brazilian university.

Dataset

The ENEM exam consists of the writing of an essay and an objective part containing 180 multiple choice questions. The questions are divided into four groups of 45 questions each, namely, Humanities, Languages, Sciences and Mathematics.

This dataset contains only the textual part of objective part, segmented in questions. Every question is divided into 3 parts:

The header, contianing background knwoledge in the form of an image, text or table is given;
The statement, containing the text of the question;
The answers, split into five fields, each containing the text of a candidate-answer (A,B,C,D,E), along with an additional tag informing the correct answer.

In addition, every question is annotated with the following tags:

id: the question number in that assessment;
image: does it has an accompanying image in the exam?
EK: does it require knowledge not given in the header?
TC: does it require textual understanding of the header?
IC: does it require understanding of the associated image?
DS: does it require complex inference or domain specific knowledge?
MR: does it requires transforming instructions in natural language into mathematical formula?
CE: does it require treating chemical elements especially?

Currently, the challenge uses an XML version of questions taken from the exams between 2009 and 2017.

A more in-depth description of the dataset is found .

Download

The 776kb zip archive containing the XML file can be downloaded

Solutions

If you design a new solution, please reporting accuracy, reference (if any) and information as below. Here is a list of current solutions:

Paper	Accuracy	Note

Citation

If you use this dataset, please cite the following work:

    @InProceedings{ ENEM-Challenge,
            author = {Silveira, Igor Cataneo and Mau\'a, Denis Deratani},
            booktitle = {Proceedings of the 6th Brazilian Conference on Intelligent Systems},
            series = {BRACIS},
            title = {University Entrance Exam as a Guiding Test for Artificial Intelligence},
            pages = {426--431},
            year = {2017}
    }

The Finite Model Theory of Bayesian Networks: Descriptive Complexity

Sun, 01 Jul 2018 00:00:00 +0000

Advances in Automatically Solving the ENEM

Mon, 01 Jan 2018 00:00:00 +0000

Robustifying sum-product networks

Mon, 01 Jan 2018 00:00:00 +0000

The complexity of Bayesian networks specified by propositional and relational languages

Mon, 01 Jan 2018 00:00:00 +0000

When a Robot Reaches Out for Human Help

Mon, 01 Jan 2018 00:00:00 +0000

Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks

Wed, 01 Mar 2017 00:00:00 +0000

Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks