10 accepted papers
Sparse Mixture-of-Experts (MoE) architectures scale model capacity efficiently but suffer from massive static parameter footprints, creating significant deployment burdens on memory-constrained hardware. Existing post-training pruning methods often rely on scalar statistics, ignoring the representat…
Few-shot knowledge graph reasoning (FS-KGR) try to infer missing facts in a knowledge graphs using limited data (such as only 3/5 samples).Existing strategies have shown good performance by mining more supervised information for few-shot learning through meta-learning and self-supervised learning. H
Recent advancements in video generation models, such as Stable Video Diffusion, have shown promising results, but these works primarily focus on short videos, often limited to a single scene and lacking a rich storyline. These models struggle with generating long videos that involve multiple scenes,…
State Space Models (SSMs), such as Mamba, have recently demonstrated potential in language understanding tasks, positioning them as competitors to transformer architectures. However, our investigations reveal that the Mamba architecture still has room for further optimization—not only in linear proj…
Delta compression methods focus on efficiently serving multiple uniquely fine-tuned models, each tailored to specific tasks and user requirements. These approaches decompose a fine-tuned LLM into a base model and corresponding delta weights, which are compressed using low-rank or low-bit representat…
Spatial transcriptomics (ST) technologies provide gene expression measurements with spatial resolution, enabling the dissection of tissue structure and function. A fundamental challenge in ST analysis is clustering spatial spots into coherent functional regions. While existing models effectively int…
Navigation through crowded intersections is a challenge for autonomous vehicles, where uncertainty arises from interaction with other road users, encountering new scenes and weathers, etc. Recent end-to-end autonomous control deep models learned from human drivers have shown promising driving perfor
As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). While quantization is a powerful approach to reducing GNNs complexity, most previous works on GNNs quantization fail to ex…
Modern autonomous driving systems face substantial challenges when navigating dense intersections due to the high uncertainty introduced by other road users. Due to the complexity of the task, the autonomous vehicle needs to generate policies at multiple levels of abstraction. However, previous deep…
Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods used in these approaches only utilize the local information of each pixel in the low-resolution multispectral (LRMS) im…