Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks

• URL: http://arxiv.org/abs/2407.00478v3
• Date: Tue, 17 Dec 2024 07:30:46 GMT
• Title: Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks
• Authors: Quanming Yao, Yongqi Zhang, Yaqing Wang, Nan Yin, James Kwok, Qiang Yang,
• Abstract summary: brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. In this paper, we attempt to address this issue in a parsimonious manner, achieving greater potential with simpler models. The key is to drive models using domain-specific knowledge, such as symbols, logic, and formulas, instead of purely relying on scaleup.
• Score: 47.6830995661091
• License:
• Abstract: The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sustainability of this strategy is a serious concern. In this paper, we attempt to address this issue in a parsimonious manner (i.e., achieving greater potential with simpler models). The key is to drive models using domain-specific knowledge, such as symbols, logic, and formulas, instead of purely relying on scaleup. This approach allows us to build a framework that uses this knowledge as "building blocks" to achieve parsimony in model design, training, and interpretation. Empirical results show that our methods surpass those that typically follow the scaling law. We also demonstrate our framework in AI for science, specifically in the problem of drug-drug interaction prediction. We hope our research can foster more diverse technical roadmaps in the era of foundation models.

Related papers

• How to Upscale Neural Networks with Scaling Law? A Survey and Practical Guidelines [20.62274005080048]
  Early research established power-law relationships in model performance, leading to compute-optimal scaling strategies. Sparse models, mixture-of-experts, retrieval-augmented learning, and multimodal models often deviate from traditional scaling patterns. scaling behaviors vary across domains such as vision, reinforcement learning, and fine-tuning, underscoring the need for more nuanced approaches.
  arXiv  Detail & Related papers  (2025-02-17T17:20:41Z)
• Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm [0.195804735329484]
  Reinforcement learning (RL) and Deep Reinforcement Learning (DRL) have the potential to disrupt and are already changing the way we interact with the world. One of the key indicators of their applicability is their ability to scale and work in real-world scenarios.
  arXiv  Detail & Related papers  (2024-08-19T14:50:48Z)
• Training Neural Networks with Internal State, Unconstrained Connectivity, and Discrete Activations [66.53734987585244]
  True intelligence may require the ability of a machine learning model to manage internal state. We show that we have not yet discovered the most effective algorithms for training such models. We present one attempt to design such a training algorithm, applied to an architecture with binary activations and only a single matrix of weights.
  arXiv  Detail & Related papers  (2023-12-22T01:19:08Z)
• Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations [1.9580473532948401]
  This thesis explores the theoretical foundations of deep learning by studying the relationship between the architecture of these models and the inherent structures found within the data they process. We ask What drives the efficacy of deep learning algorithms and allows them to beat the so-called curse of dimensionality. Our methodology takes an empirical approach to deep learning, combining experimental studies with physics-inspired toy models.
  arXiv  Detail & Related papers  (2023-10-24T19:50:41Z)
• Homological Convolutional Neural Networks [4.615338063719135]
  We propose a novel deep learning architecture that exploits the data structural organization through topologically constrained network representations. We test our model on 18 benchmark datasets against 5 classic machine learning and 3 deep learning models.
  arXiv  Detail & Related papers  (2023-08-26T08:48:51Z)
• From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks [1.0554048699217669]
  This paper proposes a novel approach to map the knowledge from action recognition to event recognition using an energy-based model. Such a model can process all frames simultaneously, carrying spatial and temporal information through the learning process.
  arXiv  Detail & Related papers  (2022-11-30T14:47:10Z)
• WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model [74.4875156387271]
  We develop a novel foundation model pre-trained with huge multimodal (visual and textual) data. We show that state-of-the-art results can be obtained on a wide range of downstream tasks.
  arXiv  Detail & Related papers  (2021-10-27T12:25:21Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• Towards Interpretable Deep Learning Models for Knowledge Tracing [62.75876617721375]
  We propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model. Experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions.
  arXiv  Detail & Related papers  (2020-05-13T04:03:21Z)
• Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples [84.8370546614042]
  Black-box nature of Deep Learning models has posed unanswered questions about what they learn from data. Generative Adversarial Network (GAN) and multi-objectives are used to furnish a plausible attack to the audited model. Its utility is showcased within a human face classification task, unveiling the enormous potential of the proposed framework.
  arXiv  Detail & Related papers  (2020-03-25T11:08:56Z)

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.