104 citations · 106 across the 31 of their papers we have counts for
17 papers · 1 filter
Latent Reasoning in TRMs is Secretly a Policy Improvement Operator
Arip Asadulaev, Rayan Banerjee, Fakhri Karray +1
Recently, small models with latent recursion have obtained promising results on complex reasoning tasks. These results are typically explained by the theory that such recursion inc…
Expert or not? assessing data quality in offline reinforcement learning
Arip Asadulaev, Fakhri Karray, Martin Takac
Offline reinforcement learning (RL) learns exclusively from static datasets, without further interaction with the environment. In practice, such datasets vary widely in quality, of…
Y-Shaped Generative Flows
Arip Asadulaev, Semyon Semenov, Abduragim Shtanchaev +3
Modern continuous-time generative models typically induce \emph{V-shaped} flows: each sample travels independently along a nearly straight trajectory from the prior to the data. Al…
A Signer-Invariant Conformer and Multi-Scale Fusion Transformer for Continuous Sign Language Recognition
Md Rezwanul Haque, Md. Milon Islam, S M Taslim Uddin Raju +1
Continuous Sign Language Recognition (CSLR) faces multiple challenges, including significant inter-signer variability and poor generalization to novel sentence structures. Traditio…
FusionEnsemble-Net: An Attention-Based Ensemble of Spatiotemporal Networks for Multimodal Sign Language Recognition
Md. Milon Islam, Md Rezwanul Haque, S M Taslim Uddin Raju +1
Accurate recognition of sign language in healthcare communication poses a significant challenge, requiring frameworks that can accurately interpret complex multimodal gestures. To…
MDD-Net: Multimodal Depression Detection through Mutual Transformer
Md Rezwanul Haque, Md. Milon Islam, S M Taslim Uddin Raju +3
Depression is a major mental health condition that severely impacts the emotional and physical well-being of individuals. The simple nature of data collection from social media pla…
MMFformer: Multimodal Fusion Transformer Network for Depression Detection
Md Rezwanul Haque, Md. Milon Islam, S M Taslim Uddin Raju +3
Depression is a serious mental health illness that significantly affects an individual's well-being and quality of life, making early detection crucial for adequate care and treatm…
GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning
S M Taslim Uddin Raju, Md. Milon Islam, Md Rezwanul Haque +2
Microscopic assessment of histopathology images is vital for accurate cancer diagnosis and treatment. Whole Slide Image (WSI) classification and captioning have become crucial task…
Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
Yuanchen Bei, Weizhi Zhang, Siwen Wang +10
AI agents have experienced a paradigm shift, from early dominance by reinforcement learning (RL) to the rise of agents powered by large language models (LLMs), and now further adva…
Bridging Brain with Foundation Models through Self-Supervised Learning
Hamdi Altaheri, Fakhri Karray, Md. Milon Islam +2
Foundation models (FMs), powered by self-supervised learning (SSL), have redefined the capabilities of artificial intelligence, demonstrating exceptional performance in domains lik…
ADAM-Dehaze: Adaptive Density-Aware Multi-Stage Dehazing for Improved Object Detection in Foggy Conditions
Fatmah AlHindaassi, Mohammed Talha Alam, Fakhri Karray
Adverse weather conditions, particularly fog, pose a significant challenge to autonomous vehicles, surveillance systems, and other safety-critical applications by severely degradin…
CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Jiahui Geng, Fengyu Cai, Shaobo Cui +8
Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks primarily emphasize functional relevance…
HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs
Qing Li, Jiahui Geng, Zongxiong Chen +5
In recent years, large language models (LLMs) have made remarkable advancements, yet hallucination, where models produce inaccurate or non-factual statements, remains a significant…
Hierarchical Instruction-aware Embodied Visual Tracking
Kui Wu, Hao Chen, Churan Wang +4
User-Centric Embodied Visual Tracking (UC-EVT) presents a novel challenge for reinforcement learning-based models due to the substantial gap between high-level user instructions an…
VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration
Jiahui Geng, Qing Li, Zongxiong Chen +7
The rapid advancement of vision-language models (VLMs) has brought a lot of attention to their safety alignment. However, existing methods have primarily focused on model undersafe…
A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models
Jiahui Geng, Qing Li, Herbert Woisetschlaeger +6
This study investigates the machine unlearning techniques within the context of large language models (LLMs), referred to as \textit{LLM unlearning}. LLM unlearning offers a princi…
Internal Activation Revision: Safeguarding Vision Language Models Without Parameter Update
Qing Li, Jiahui Geng, Zongxiong Chen +3
Vision-language models (VLMs) demonstrate strong multimodal capabilities but have been found to be more susceptible to generating harmful content compared to their backbone large l…