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Contrastive Sampling Chains in Diffusion Models
The code is available at Contrastive-Sampling .
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation Wei Dong
In this work, we propose a novel parameter-efficient adaptation method to fine-tune pre-trained ViTs.
The Behavior and Convergence of Local Bayesian Optimization
The "folk wisdom" in the literature is that the focus on local optimization sidesteps the curse of dimensionality;
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment. We observe that due to the high complexity in the training objective of panoptic segmentation, it will inevitably lead to much higher penalization on false positive.
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model
This dual approach bridges the semantic gap between visual and textual data, thereby improving the model's ability to process and interpret information from multiple images effectively.
Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning
Sutton et al., 2022], is elegantly captured using the options framework [Sutton et al., 1999].
Preference Learning Algorithms Do Not Learn Preference Rankings
Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner workings is still limited.