Publications

Asymmetry in the complexity of the multi-commodity network pricing problem

Quang Minh Bui

Margarida Carvalho

José Neto

2024-01-01

Math. Program. (published)

doi.org

arxiv.org

Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptive Object Detection

Atif Belal

Akhil Meethal

Francisco Perdigon Romero

Marco Pedersoli

Eric Granger

2024-01-01

arXiv.org (preprint)

doi.org

Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptive Object Detection

Atif Belal

Akhil Meethal

Francisco Perdigon Romero

Marco Pedersoli

Eric Granger

2024-01-01

arXiv.org (preprint)

doi.org

An Attentive Approach for Building Partial Reasoning Agents from Pixels

Safa Alver

Doina Precup

We study the problem of building reasoning agents that are able to generalize in an effective manner. Towards this goal, we propose an end-t… (see more)o-end approach for building model-based reinforcement learning agents that dynamically focus their reasoning to the relevant aspects of the environment: after automatically identifying the distinct aspects of the environment, these agents dynamically filter out the relevant ones and then pass them to their simulator to perform partial reasoning. Unlike existing approaches, our approach works with pixel-based inputs and it allows for interpreting the focal points of the agent. Our quantitative analyses show that the proposed approach allows for effective generalization in high-dimensional domains with raw observational inputs. We also perform ablation analyses to validate our design choices. Finally, we demonstrate through qualitative analyses that our approach actually allows for building agents that focus their reasoning on the relevant aspects of the environment.

2024-01-01

Trans. Mach. Learn. Res. (published)

openreview.net

BAND: Biomedical Alert News Dataset

Zihao Fu

Meiru Zhang

Zaiqiao Meng

Yannan Shen

Anya Okhmatovskaia

David Buckeridge

Nigel Collier

2024-01-01

AAAI (published)

doi.org

arxiv.org

Benchmarking Vision Language Models for Cultural Understanding

Sjoerd van Steenkiste

Lisa Anne Hendricks

Karolina Stanczak

Aishwarya Agrawal

Foundation models and vision-language pre-training have notably advanced Vision Language Models (VLMs), enabling multimodal processing of vi… (see more)sual and linguistic data. However, their performance has been typically assessed on general scene understanding - recognizing objects, attributes, and actions - rather than cultural comprehension. This study introduces CulturalVQA, a visual question-answering benchmark aimed at assessing VLM's geo-diverse cultural understanding. We curate a collection of 2,378 image-question pairs with 1-5 answers per question representing cultures from 11 countries across 5 continents. The questions probe understanding of various facets of culture such as clothing, food, drinks, rituals, and traditions. Benchmarking VLMs on CulturalVQA, including GPT-4V and Gemini, reveals disparity in their level of cultural understanding across regions, with strong cultural understanding capabilities for North America while significantly lower performance for Africa. We observe disparity in their performance across cultural facets too, with clothing, rituals, and traditions seeing higher performances than food and drink. These disparities help us identify areas where VLMs lack cultural understanding and demonstrate the potential of CulturalVQA as a comprehensive evaluation set for gauging VLM progress in understanding diverse cultures.

2024-01-01

EMNLP (published)

doi.org

arxiv.org

BETAC: Bidirectional Encoder Transformer for Assembly Code Function Name Recovery

Guillaume Breyton

Mohd Saqib

Benjamin Fung

Philippe Charland

Recovering function names from stripped binaries is a crucial and time-consuming task for software reverse engineering’ particularly in en… (see more)hancing network reliability, resilience, and security. This paper tackles the challenge of recovering function names in stripped binaries, a fundamental step in reverse engineering. The absence of syntactic information and the possibility of different code producing identical behavior complicate this task. To overcome these challenges, we introduce a novel model, the Bidirectional Encoder Transformer for Assembly Code (BETAC), leveraging a transformer-based architecture known for effectively processing sequential data. BETAC utilizes self-attention mechanisms and feed-forward networks to discern complex relationships within assembly code for precise function name prediction. We evaluated BETAC against various existing encoder and decoder models in diverse binary datasets, including benign and malicious codes in multiple formats. Our model demonstrated superior performance over previous techniques in certain metrics and showed resilience against code obfuscation.

2024-01-01

DRCN (published)

doi.org

Beyond Model Collapse: Scaling Up with Synthesized Data Requires Reinforcement

Yunzhen Feng

Elvis Dohmatob

Pu Yang

Francois Charton

Julia Kempe

2024-01-01

arXiv.org (preprint)

doi.org