2k citations · 2.7k across the 55 of their papers we have counts for
59 papers
Beyond Rule-Based Mutation Testing: Test-Aware Mutant Generation Using Large Language Models
Nils Kiele, Zainab Saad, Zirui Wang +2
Mutation testing evaluates test-suite adequacy by injecting synthetic faults into program code. However, traditional rule-based tools often generate large numbers of trivial, redun…
REBOOT: From Failure to Recovery - A Dataset and Benchmark for Precision Assembly
Nana Yaw Owusu Ofori-Ampofo, Samira Ebrahimi Kahou, Joseph Thekinen
Robot learning policies fail in characteristic ways: they stall in uncertain states, drift during contact-rich alignment, and miss targets by millimetres in precision tasks. Yet tr…
Training Fair Tabular Foundation Models
Patrik Kenfack, Jesse C. Cresswell, Anthony L. Caterini +2
Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training.…
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
Vector Quantized Latent Concepts: A Scalable Alternative to Clustering-Based Concept Discovery
Xuemin Yu, Ankur Garg, Samira Ebrahimi Kahou +1
Large language models (LLMs) encode rich semantic information in their hidden states, yet it remains difficult to understand what information these internal representations capture…
Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
Zihan Wang, Samira Ebrahimi Kahou, Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable features rather than requiring any labeled exa…
GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities
Diganta Misra, Nizar Islah, Victor May +9
The rapid evolution of software libraries poses a considerable hurdle for code generation, necessitating continuous adaptation to frequent version updates while preserving backward…
Learning From the Past with Cascading Eligibility Traces
Tokiniaina Raharison Ralambomihanta, Ivan Anokhin, Roman Pogodin +3
Animals often receive information about errors and rewards after a significant delay. For example, there is typically a delay of tens to hundreds of milliseconds between motor acti…
Estimation of Head Motion in Structural MRI and its Impact on Cortical Thickness Measurements in Retrospective Data
Charles Bricout, Samira Ebrahimi Kahou, Sylvain Bouix
Motion-related artifacts are inevitable in Magnetic Resonance Imaging (MRI) and can bias automated neuroanatomical metrics such as cortical thickness. These biases can interfere wi…
Cross-Layer Discrete Concept Discovery for Interpreting Language Models
Ankur Garg, Xuemin Yu, Hassan Sajjad +1
Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across adjacent layers, causing single-layer…
Towards Fair In-Context Learning with Tabular Foundation Models
Patrik Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji
Transformer-based tabular foundation models have recently demonstrated promising in-context learning (ICL) performance on structured data, emerging as competitive alternatives to g…
Handling Delay in Real-Time Reinforcement Learning
Ivan Anokhin, Rishav Rishav, Matthew Riemer +3
Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second due to hardware limitations. Second, th…
Behaviour Discovery and Attribution for Explainable Reinforcement Learning
Rishav Rishav, Somjit Nath, Vincent Michalski +1
Building trust in reinforcement learning (RL) agents requires understanding why they make certain decisions, especially in high-stakes applications like robotics, healthcare, and f…
Improving Quality Control Of MRI Images Using Synthetic Motion Data
Charles Bricout, Kang Ik K. Cho, Michael Harms +10
MRI quality control (QC) is challenging due to unbalanced and limited datasets, as well as subjective scoring, which hinder the development of reliable automated QC systems. To add…
Comparative Analysis of Diffusion Generative Models in Computational Pathology
Denisha Thakkar, Vincent Quoc-Huy Trinh, Sonal Varma +3
Diffusion Generative Models (DGM) have rapidly surfaced as emerging topics in the field of computer vision, garnering significant interest across a wide array of deep learning appl…
Adaptive Group Robust Ensemble Knowledge Distillation
Patrik Kenfack, Ulrich Aïvodji, Samira Ebrahimi Kahou
Neural networks can learn spurious correlations in the data, often leading to performance degradation for underrepresented subgroups. Studies have demonstrated that the disparity i…
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
Rambod Azimi, Rishav Rishav, Marek Teichmann +1
Large language models (LLMs) have demonstrated remarkable performance across various downstream tasks. However, the high computational and memory requirements of LLMs are a major b…
Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks
Riadh Azzaz, Valentin Hurel, Patrice Menard +3
The scrap-based electric arc furnace process is expected to capture a significant share of the steel market in the future due to its potential for reducing environmental impacts th…
Learning Multi-agent Multi-machine Tending by Mobile Robots
Abdalwhab Abdalwhab, Giovanni Beltrame, Samira Ebrahimi Kahou +1
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also high…
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
Aamer Abdul Rahman, Pranav Agarwal, Rita Noumeir +3
Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its application, however, has been limited by the…
Reinforcement Learning for Sequence Design Leveraging Protein Language Models
Jithendaraa Subramanian, Shivakanth Sujit, Niloy Irtisam +4
Protein sequence design, determined by amino acid sequences, are essential to protein engineering problems in drug discovery. Prior approaches have resorted to evolutionary strateg…
Learning to Play Atari in a World of Tokens
Pranav Agarwal, Sheldon Andrews, Samira Ebrahimi Kahou
Model-based reinforcement learning agents utilizing transformers have shown improved sample efficiency due to their ability to model extended context, resulting in more accurate wo…
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization
Jordi Armengol-Estapé, Vincent Michalski, Ramnath Kumar +3
Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that cross-modal learning can improve representati…
Spectral Temporal Contrastive Learning
Sacha Morin, Somjit Nath, Samira Ebrahimi Kahou +1
Learning useful data representations without requiring labels is a cornerstone of modern deep learning. Self-supervised learning methods, particularly contrastive learning (CL), ha…