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Found 12 papers, 10 papers with code
ChestX-Reasoner: Advancing Radiology Foundation Models with Reasoning through Step-by-Step Verification
Recent advances in reasoning-enhanced large language models (LLMs) and multimodal LLMs (MLLMs) have significantly improved performance in complex tasks, yet medical AI models often overlook the structured reasoning processes inherent in clinical practice.
Continual Task Learning through Adaptive Policy Self-Composition
Training a generalizable agent to continually learn a sequence of tasks from offline trajectories is a natural requirement for long-lived agents, yet remains a significant challenge for current offline reinforcement learning (RL) algorithms.
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy applicable to diverse tasks without the need for online environmental interaction.
Reconstruct the Pruned Model without Any Retraining
Structured pruning is a promising hardware-friendly compression technique for large language models (LLMs), which is expected to be retraining-free to avoid the enormous retraining cost.
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
Partially class-disjoint data (PCDD), a common yet under-explored data formation where each client contributes a part of classes (instead of all classes) of samples, severely challenges the performance of federated algorithms.
Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts
This paper presents a Domain-Inspired Sharpness-Aware Minimization (DISAM) algorithm for optimization under domain shifts.
Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping
Statistical heterogeneity severely limits the performance of federated learning (FL), motivating several explorations e. g., FedProx, MOON and FedDyn, to alleviate this problem.
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization
However, the local loss landscapes may not accurately reflect the flatness of global loss landscape in heterogeneous environments; as a result, minimizing local sharpness and calculating perturbations on client data might not align the efficacy of SAM in FL with centralized training.
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
However, variations in task content and complexity pose significant challenges in policy formulation, necessitating judicious parameter sharing and management of conflicting gradients for optimal policy performance.
Q-value Regularized Transformer for Offline Reinforcement Learning
Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action distribution based on history trajectory and target returns for each state.
FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation
However, in addition to previous explorations for improvement in federated averaging, our analysis shows that another critical bottleneck is the poorer optima of client models in more heterogeneous conditions.
Disentangling Hate in Online Memes
Our experiment results show that DisMultiHate is able to outperform state-of-the-art unimodal and multimodal baselines in the hateful meme classification task.
Ranked #7 on
Hateful Meme Classification
on HarMeme
