1 citations · 1 across the 4 of their papers we have counts for
5 papers
Selective Backpropagation for Efficient Few-Shot Class-Incremental Learning
Eeham Khan, Abdulmoumen Al-Atrash, Ali Ayub
Few-Shot Class-Incremental Learning (FSCIL) requires models to continuously learn new classes from limited samples while retaining prior knowledge, under strict constraints on comp…
CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach
Andrei Neagu, Eeham Khan, Leila Kosseim
This paper presents our system for Task 3 of the CLEF 2026 FinMMEval Lab, which requires daily long, flat, or short trading decisions for Bitcoin (BTC) and Tesla (TSLA) using news…
Reason and Verify: A Framework for Faithful Retrieval-Augmented Generation
Eeham Khan, Luis Rodriguez, Marc Queudot
Retrieval-Augmented Generation (RAG) significantly improves the factuality of Large Language Models (LLMs), yet standard pipelines often lack mechanisms to verify inter- mediate re…
Low-Resource Dialect Adaptation of Large Language Models: A French Dialect Case-Study
Eeham Khan, Firas Saidani, Owen Van Esbroeck +2
Despite the widespread adoption of Large Language Models (LLMs), their strongest capabilities remain largely confined to a small number of high-resource languages for which there i…
CLaC at SemEval-2025 Task 6: A Multi-Architecture Approach for Corporate Environmental Promise Verification
Nawar Turk, Eeham Khan, Leila Kosseim
This paper presents our approach to the SemEval-2025 Task~6 (PromiseEval), which focuses on verifying promises in corporate ESG (Environmental, Social, and Governance) reports. We…