6 papers
Uncovering Latent Communication Patterns in Brain Networks via Adaptive Flow Routing
Tianhao Huang, Guanghui Min, Zhenyu Lei +2
Unraveling how macroscopic cognitive phenotypes emerge from microscopic neuronal connectivity remains one of the core pursuits of neuroscience. To this end, researchers typically l…
Post-LayerNorm Is Back: Stable, ExpressivE, and Deep
Chen Chen, Lai Wei
Large language model (LLM) scaling is hitting a wall. Widening models yields diminishing returns, and extending context length does not improve fundamental expressivity. In contras…
Monkey Jump : MoE-Style PEFT for Efficient Multi-Task Learning
Nusrat Jahan Prottasha, Md Kowsher, Chun-Nam Yu +2
Mixture-of-experts variants of parameter-efficient fine-tuning enable per-token specialization, but they introduce additional trainable routers and expert parameters, increasing me…
SliceFine: The Universal Winning-Slice Hypothesis for Pretrained Networks
Md Kowsher, Ali O. Polat, Ehsan Mohammady Ardehaly +4
This paper presents a theoretical framework explaining why fine tuning small, randomly selected subnetworks (slices) within pre trained models can be sufficient for downstream adap…
FlowNIB: An Information Bottleneck Analysis of Bidirectional vs. Unidirectional Language Models
Md Kowsher, Nusrat Jahan Prottasha, Shiyun Xu +4
Bidirectional language models have better context understanding and perform better than unidirectional models on natural language understanding tasks, yet the theoretical reasons b…
Predicting Through Generation: Why Generation Is Better for Prediction
Md Kowsher, Nusrat Jahan Prottasha, Prakash Bhat +6
This paper argues that generating output tokens is more effective than using pooled representations for prediction tasks because token-level generation retains more mutual informat…