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Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis Y uxi Ren Xin Xia Y anzuo Lu Jiacheng Zhang Jie Wu Pan Xie Xing Wang Xuefeng Xiao
Recently, a series of diffusion-aware distillation algorithms have emerged to alleviate the computational overhead associated with the multi-step inference process of Diffusion Models (DMs).
Conditional Density Estimation with Histogram Trees
This makes CDE particularly useful in critical application domains. However, interpretable CDE methods are understudied.
Navigating Extremes: Dynamic Sparsity in Large Output Spaces
In recent years, Dynamic Sparse Training (DST) has emerged as an alternative to post-training pruning for generating efficient models. In principle, DST allows for a more memory efficient training process, as it maintains sparsity throughout the entire training run. However, current DST implementations fail to capitalize on this in practice. Because sparse matrix multiplication is much less efficient than dense matrix multiplication on GPUs, most implementations simulate sparsity by masking weights.
Learning Low-Rank Feature for Thorax Disease Classification
We study thorax disease classification in this paper.
Reinforcement Learning with L TL and ฯ-Regular Objectives via Optimality-Preserving Translation to Average Rewards Xuan-Bach Le
In this work, we study the relationship between these objectives.
Conformalized Credal Set Predictors
However, the design of methods for learning credal set predictors remains a challenging problem. In this paper, we make use of conformal prediction for this purpose.
Soft Superpixel Neighborhood Attention
Images contain objects with deformable boundaries, such as the contours of a human face, yet attention operators act on square windows.
Uncovering Safety Risks of Large Language Models through Concept Activation Vector
Warning: This paper contains text examples that are offensive or harmful in nature.
