Maximum Manifold Capacity Representations in State Representation Learning

• URL: http://arxiv.org/abs/2405.13848v1
• Date: Wed, 22 May 2024 17:19:30 GMT
• Title: Maximum Manifold Capacity Representations in State Representation Learning
• Authors: Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad,
• Abstract summary: manifold-based self-supervised learning (SSL) builds on the manifold hypothesis. DeepInfomax with an unbalanced atlas (DIM-UA) has emerged as a powerful tool. MMCR presents a new frontier for SSL by optimizing class separability via manifold compression. We present an innovative integration of MMCR into existing SSL methods, incorporating a discerning regularization strategy.
• Score: 8.938418994111716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The expanding research on manifold-based self-supervised learning (SSL) builds on the manifold hypothesis, which suggests that the inherent complexity of high-dimensional data can be unraveled through lower-dimensional manifold embeddings. Capitalizing on this, DeepInfomax with an unbalanced atlas (DIM-UA) has emerged as a powerful tool and yielded impressive results for state representations in reinforcement learning. Meanwhile, Maximum Manifold Capacity Representation (MMCR) presents a new frontier for SSL by optimizing class separability via manifold compression. However, MMCR demands extensive input views, resulting in significant computational costs and protracted pre-training durations. Bridging this gap, we present an innovative integration of MMCR into existing SSL methods, incorporating a discerning regularization strategy that enhances the lower bound of mutual information. We also propose a novel state representation learning method extending DIM-UA, embedding a nuclear norm loss to enforce manifold consistency robustly. On experimentation with the Atari Annotated RAM Interface, our method improves DIM-UA significantly with the same number of target encoding dimensions. The mean F1 score averaged over categories is 78% compared to 75% of DIM-UA. There are also compelling gains when implementing SimCLR and Barlow Twins. This supports our SSL innovation as a paradigm shift, enabling more nuanced high-dimensional data representations.

Related papers

• Towards Scalable Semantic Representation for Recommendation [65.06144407288127]
  Mixture-of-Codes is proposed to construct semantic IDs based on large language models (LLMs) Our method achieves superior discriminability and dimension robustness scalability, leading to the best scale-up performance in recommendations.
  arXiv  Detail & Related papers  (2024-10-12T15:10:56Z)
• Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models [66.1595537904019]
  Large language models (LLMs) can act as gradient priors in a zero-shot setting. We introduce LM-GC, a novel method that integrates LLMs with arithmetic coding.
  arXiv  Detail & Related papers  (2024-09-26T13:38:33Z)
• Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations [29.69549286641418]
  Maximum Manifold Capacity Representations (MMCR) is a self-supervised learning method that matches or surpasses other leading MVSSL methods. We show that MMCR incentivizes alignment and uniformity of learned embeddings. We experimentally confirm non-monotonic changes in the pretraining loss akin to double descent. We then show that MMCR, originally applied to image data, is performant on multimodal image-text data.
  arXiv  Detail & Related papers  (2024-06-13T17:49:56Z)
• Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs [7.923088041693465]
  We present an ensemble of lightweight VAEs to learn posteriors over subsets of the feature-space, which get aggregated into a joint posterior in a novel divide-and-conquer approach. We show that our approach is robust to partial features at inference, exhibiting little performance degradation even with most features missing.
  arXiv  Detail & Related papers  (2023-06-27T17:55:31Z)
• State Representation Learning Using an Unbalanced Atlas [8.938418994111716]
  This paper introduces a novel learning paradigm using an unbalanced atlas (UA), capable of surpassing state-of-the-art self-supervised learning approaches. The efficacy of DIM-UA is demonstrated through training and evaluation on the Atari Annotated RAM Interface benchmark.
  arXiv  Detail & Related papers  (2023-05-17T14:58:58Z)
• Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations [4.666056064419346]
  The efficient coding hypothesis proposes that the response properties of sensory systems are adapted to the statistics of their inputs. While elegant, information theoretic properties are notoriously difficult to measure in practical settings or to employ as objective functions in optimization. Here we outline the assumptions that allow manifold capacity to be optimized directly, yielding Maximum Manifold Capacity Representations (MMCR)
  arXiv  Detail & Related papers  (2023-03-06T17:26:30Z)
• Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution [48.093500219958834]
  We propose an Accelerated Multi-Scale Aggregation network (AMSA) for Reference-based Super-Resolution. The proposed AMSA achieves superior performance over state-of-the-art approaches on both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2022-01-12T08:40:23Z)
• Adaptive Hierarchical Similarity Metric Learning with Noisy Labels [138.41576366096137]
  We propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, textiti.e., class-wise divergence and sample-wise consistency. Our method achieves state-of-the-art performance compared with current deep metric learning approaches.
  arXiv  Detail & Related papers  (2021-10-29T02:12:18Z)
• Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning [54.446686397551275]
  We present RL-MMR, which unifies advanced neural SDS methods and statistical measures used in classical MDS. RL-MMR casts MMR guidance on fewer promising candidates, which restrains the search space and thus leads to better representation learning.
  arXiv  Detail & Related papers  (2020-09-30T21:50:46Z)
• Reinforcement Learning with Augmented Data [97.42819506719191]
  We present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms. We show that augmentations such as random translate, crop, color jitter, patch cutout, random convolutions, and amplitude scale can enable simple RL algorithms to outperform complex state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-04-30T17:35:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.