On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning

• URL: http://arxiv.org/abs/2404.02254v2
• Date: Wed, 17 Jul 2024 17:01:45 GMT
• Title: On Stronger Computational Separations Between Multimodal and Unimodal Machine Learning
• Authors: Ari Karchmer,
• Abstract summary: Lu (NeurIPS '23, ALT '24) introduces a theory of multimodal learning. In particular, Lu (ALT '24) shows a computational separation, which is relevant to textitworst-case instances of the learning task. We prove that under basic conditions, any given computational separation between average-case unimodal and multimodal learning tasks implies a corresponding cryptographic key agreement protocol.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, multimodal machine learning has enjoyed huge empirical success (e.g. GPT-4). Motivated to develop theoretical justification for this empirical success, Lu (NeurIPS '23, ALT '24) introduces a theory of multimodal learning, and considers possible \textit{separations} between theoretical models of multimodal and unimodal learning. In particular, Lu (ALT '24) shows a computational separation, which is relevant to \textit{worst-case} instances of the learning task. In this paper, we give a stronger \textit{average-case} computational separation, where for ``typical'' instances of the learning task, unimodal learning is computationally hard, but multimodal learning is easy. We then question how ``natural'' the average-case separation is. Would it be encountered in practice? To this end, we prove that under basic conditions, any given computational separation between average-case unimodal and multimodal learning tasks implies a corresponding cryptographic key agreement protocol. We suggest to interpret this as evidence that very strong \textit{computational} advantages of multimodal learning may arise \textit{infrequently} in practice, since they exist only for the ``pathological'' case of inherently cryptographic distributions. However, this does not apply to possible (super-polynomial) \textit{statistical} advantages.

Related papers

• On the Comparison between Multi-modal and Single-modal Contrastive Learning [50.74988548106031]
  We introduce a theoretical foundation for understanding the differences between multi-modal and single-modal contrastive learning. We identify the critical factor, which is the signal-to-noise ratio (SNR), that impacts the generalizability in downstream tasks of both multi-modal and single-modal contrastive learning. Our analysis provides a unified framework that can characterize the optimization and generalization of both single-modal and multi-modal contrastive learning.
  arXiv  Detail & Related papers  (2024-11-05T06:21:17Z)
• MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance [10.580712937465032]
  We identify the previously ignored gradient conflict between multimodal and unimodal learning objectives. We propose MMPareto algorithm, which could ensure a final gradient with direction common to all learning objectives. Our method is also expected to facilitate multi-task cases with a clear discrepancy in task difficulty.
  arXiv  Detail & Related papers  (2024-05-28T01:19:13Z)
• What Makes Multimodal In-Context Learning Work? [58.48612721156335]
  We present a framework for investigating Multimodal ICL (M-ICL) in the context of Large Multimodal Models. M-ICL primarily relies on text-driven mechanisms, showing little to no influence from the image modality. We identify several biases and limitations of M-ICL that warrant consideration prior to deployment.
  arXiv  Detail & Related papers  (2024-04-24T08:50:45Z)
• On the Computational Benefit of Multimodal Learning [3.4991031406102238]
  We show that, under certain conditions, multimodal learning can outpace unimodal learning exponentially in terms of computation. Specifically, we present a learning task that is NP-hard for unimodal learning but is solvable in time by a multimodal algorithm.
  arXiv  Detail & Related papers  (2023-09-25T00:20:50Z)
• A Theory of Multimodal Learning [3.4991031406102238]
  The study of multimodality remains relatively under-explored within the field of machine learning. An intriguing finding is that a model trained on multiple modalities can outperform a finely-tuned unimodal model, even on unimodal tasks. This paper provides a theoretical framework that explains this phenomenon, by studying generalization properties of multimodal learning algorithms.
  arXiv  Detail & Related papers  (2023-09-21T20:05:49Z)
• Deep Metric Loss for Multimodal Learning [3.8979646385036175]
  We introduce a novel textMultiModal loss paradigm for multimodal learning. textMultiModal loss can prevent inefficient learning caused by overfitting and efficiently optimize multimodal models. Our loss is empirically shown to improve the performance of recent models.
  arXiv  Detail & Related papers  (2023-08-21T06:04:30Z)
• Language models are weak learners [71.33837923104808]
  We show that prompt-based large language models can operate effectively as weak learners. We incorporate these models into a boosting approach, which can leverage the knowledge within the model to outperform traditional tree-based boosting. Results illustrate the potential for prompt-based LLMs to function not just as few-shot learners themselves, but as components of larger machine learning pipelines.
  arXiv  Detail & Related papers  (2023-06-25T02:39:19Z)
• Learning Unseen Modality Interaction [54.23533023883659]
  Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. We pose the problem of unseen modality interaction and introduce a first solution. It exploits a module that projects the multidimensional features of different modalities into a common space with rich information preserved.
  arXiv  Detail & Related papers  (2023-06-22T10:53:10Z)
• Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning [79.83792914684985]
  We prove a new identifiability result that provides conditions under which maximally sparse base-predictors yield disentangled representations. Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem.
  arXiv  Detail & Related papers  (2022-11-26T21:02:09Z)
• Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably) [75.38159612828362]
  It has been observed that the best uni-modal network outperforms the jointly trained multi-modal network. This work provides a theoretical explanation for the emergence of such performance gap in neural networks for the prevalent joint training framework.
  arXiv  Detail & Related papers  (2022-03-23T06:21:53Z)
• What Makes Multimodal Learning Better than Single (Provably) [28.793128982222438]
  We show that learning with multiple modalities achieves a smaller population risk thanonly using its subset of modalities. This is the first theoretical treatment to capture important qualitative phenomenaobserved in real multimodal applications.
  arXiv  Detail & Related papers  (2021-06-08T17:20:02Z)

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.