Employing Layerwised Unsupervised Learning to Lessen Data and Loss Requirements in Forward-Forward Algorithms

• URL: http://arxiv.org/abs/2404.14664v1
• Date: Tue, 23 Apr 2024 01:49:12 GMT
• Title: Employing Layerwised Unsupervised Learning to Lessen Data and Loss Requirements in Forward-Forward Algorithms
• Authors: Taewook Hwang, Hyein Seo, Sangkeun Jung,
• Abstract summary: Forward-Forward algorithm trains deep learning models solely through the forward pass. We propose an Unsupervised Forward-Forward algorithm to replace back-propagation. We lead to stable learning and enable versatile utilization across various datasets and tasks.
• Score: 1.0514231683620516
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent deep learning models such as ChatGPT utilizing the back-propagation algorithm have exhibited remarkable performance. However, the disparity between the biological brain processes and the back-propagation algorithm has been noted. The Forward-Forward algorithm, which trains deep learning models solely through the forward pass, has emerged to address this. Although the Forward-Forward algorithm cannot replace back-propagation due to limitations such as having to use special input and loss functions, it has the potential to be useful in special situations where back-propagation is difficult to use. To work around this limitation and verify usability, we propose an Unsupervised Forward-Forward algorithm. Using an unsupervised learning model enables training with usual loss functions and inputs without restriction. Through this approach, we lead to stable learning and enable versatile utilization across various datasets and tasks. From a usability perspective, given the characteristics of the Forward-Forward algorithm and the advantages of the proposed method, we anticipate its practical application even in scenarios such as federated learning, where deep learning layers need to be trained separately in physically distributed environments.

Related papers

• A Unified Framework for Neural Computation and Learning Over Time [56.44910327178975]
  Hamiltonian Learning is a novel unified framework for learning with neural networks "over time" It is based on differential equations that: (i) can be integrated without the need of external software solvers; (ii) generalize the well-established notion of gradient-based learning in feed-forward and recurrent networks; (iii) open to novel perspectives.
  arXiv  Detail & Related papers  (2024-09-18T14:57:13Z)
• S-EPOA: Overcoming the Indivisibility of Annotations with Skill-Driven Preference-Based Reinforcement Learning [7.8063180607224165]
  Preference-based reinforcement learning (PbRL) uses human preferences as a direct reward signal. Traditional PbRL methods are often constrained by the indivisibility of annotations, which impedes the learning process.
  arXiv  Detail & Related papers  (2024-08-22T04:54:25Z)
• Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization [53.15874572081944]
  We study computability in the deep learning framework from two perspectives. We show algorithmic limitations in training deep neural networks even in cases where the underlying problem is well-behaved. Finally, we show that in quantized versions of classification and deep network training, computability restrictions do not arise or can be overcome to a certain degree.
  arXiv  Detail & Related papers  (2024-08-12T15:02:26Z)
• 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)
• A Study of Forward-Forward Algorithm for Self-Supervised Learning [65.268245109828]
  We study the performance of forward-forward vs. backpropagation for self-supervised representation learning. Our main finding is that while the forward-forward algorithm performs comparably to backpropagation during (self-supervised) training, the transfer performance is significantly lagging behind in all the studied settings.
  arXiv  Detail & Related papers  (2023-09-21T10:14:53Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity Recognition [2.2082422928825136]
  Self-supervised learning is typically used to learn deep feature representations from unlabeled data. We propose integrating a dynamic time warping algorithm in a latent space to force features to be aligned in a temporal dimension. The proposed approach has a great potential in learning robust feature representations compared to the recent SSL baselines.
  arXiv  Detail & Related papers  (2022-10-07T07:51:01Z)
• Emphatic Algorithms for Deep Reinforcement Learning [43.17171330951343]
  Temporal difference learning algorithms can become unstable when combined with function approximation and off-policy sampling. Emphatic temporal difference (ETD($lambda$) algorithm ensures convergence in the linear case by appropriately weighting the TD($lambda$) updates. We show that naively adapting ETD($lambda$) to popular deep reinforcement learning algorithms, which use forward view multi-step returns, results in poor performance.
  arXiv  Detail & Related papers  (2021-06-21T12:11:39Z)
• Unbiased Deep Reinforcement Learning: A General Training Framework for Existing and Future Algorithms [3.7050607140679026]
  We propose a novel training framework that is conceptually comprehensible and potentially easy to be generalized to all feasible algorithms for reinforcement learning. We employ Monte-carlo sampling to achieve raw data inputs, and train them in batch to achieve Markov decision process sequences. We propose several algorithms embedded with our new framework to deal with typical discrete and continuous scenarios.
  arXiv  Detail & Related papers  (2020-05-12T01:51:08Z)
• Continual Deep Learning by Functional Regularisation of Memorable Past [95.97578574330934]
  Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past. We propose a new functional-regularisation approach that utilises a few memorable past examples crucial to avoid forgetting. Our method achieves state-of-the-art performance on standard benchmarks and opens a new direction for life-long learning where regularisation and memory-based methods are naturally combined.
  arXiv  Detail & Related papers  (2020-04-29T10:47:54Z)

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.