Our work tackles the computational challenges of contrastive learning methods, particularly for the pretraining of Vision Transformers (ViTs). Despite the effectiveness of contrastive learning, the substantial computational resources required for training often hinder their practical application. To mitigate this issue, we propose an acceleration framework, leveraging ViT's unique ability to generalize across inputs of varying sequence lengths. Our method employs a mix of sequence compression strategies, including randomized token dropout and flexible patch scaling, to reduce the cost of gradient estimation and accelerate convergence. We further provide an in-depth analysis of the gradient estimation error of various acceleration strategies as well as their impact on downstream tasks, offering valuable insights into the trade-offs between acceleration and performance. We also propose a novel procedure to identify an optimal acceleration schedule to adjust the sequence compression ratios to the training progress, ensuring efficient training without sacrificing downstream performance. Our approach significantly reduces computational overhead across various self-supervised learning algorithms on large-scale datasets. In ImageNet, our method achieves speedups of 4$\times$ in MoCo, 3.3$\times$ in SimCLR, and 2.5$\times$ in DINO, demonstrating substantial efficiency gains.

In this paper, we investigate the benefits of self-supervised learning (SSL) to downstream tasks of satellite images. Unlike common student academic projects, this work focuses on the advantages of the SSL for deployment-driven tasks which have specific scenarios with low or high-spatial resolution images. Our preliminary experiments demonstrate the robust benefits of the SSL trained by medium-resolution (10m) images to both low-resolution (100m) scene classification case (4.25%↑) and very high-resolution (5cm) aerial image segmentation case (1.96%↑), respectively.

In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Building upon ideas from preference-based bandit learning and posterior sampling in RL, we present DUELING POSTERIOR SAMPLING (DPS), which employs preference-based posterior sampling to learn both the system dynamics and the underlying utility function that governs the preference feedback. As preference feedback is provided on trajectories rather than individual state-action pairs, we develop a Bayesian approach for the credit assignment problem, translating preferences to a posterior distribution over state-action reward models. We prove an asymptotic Bayesian no-regret rate for DPS with a Bayesian linear regression credit assignment model. This is the first regret guarantee for preference-based RL to our knowledge. We also discuss possible avenues for extending the proof methodology to other credit assignment models. Finally, we evaluate the approach empirically, showing competitive performance against existing baselines.

In many real tasks, side information in addition to the observed entries is available in the matrix completion problem. To make good use of this information, an inductive approach to matrix completion was proposed where the matrix entries are modeled as a bilinear function of real-valued vectors associated with the rows and the columns. However, it is not effective in handling data of nonlinear structures. In this paper, we propose a novel model called Deep Inductive Matrix Completion (DIMC) for nonlinear inductive matrix completion, which consists of two deep-structure neural networks to extract latent features from high-dimensional known side vectors, and then to predict their relationships using the latent features. In DIMC, the parameters of the neural networks are alternatively optimized to minimize the reconstruction error. Then the missing entries can be readily recovered with the side vectors of rows and columns. We compare DIMC with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of drug repositioning, gene-disease and miRNA-disease association prediction. The experimental results verified that DIMC is capable to provide higher accuracy than existing methods and is applicable to predict inductively on new row-column interactions with auxiliary side information. In addition, we discuss the effects of alternating training frequency on the performance of DIMC and how we can utilize such property to implement a GPU-based parallel computing algorithm that significantly shortens the training time.