GPU-Accelerated Rule Evaluation and Evolution

• URL: http://arxiv.org/abs/2406.01821v1
• Date: Mon, 3 Jun 2024 22:24:12 GMT
• Title: GPU-Accelerated Rule Evaluation and Evolution
• Authors: Hormoz Shahrzad, Risto Miikkulainen,
• Abstract summary: This paper introduces an innovative approach to boost the efficiency and scalability of Evolutionary Rule-based machine Learning (ERL) The method proposed in this paper, AERL (Accelerated ERL) solves this problem in two ways. First, by adopting GPU-optimized rule sets through a tensorized representation within the PyTorch framework, AERL mitigates the bottleneck and accelerates fitness evaluation significantly. Second, AERL takes further advantage of the GPU by fine-tuning the rule coefficients via back-propagation, thereby improving search space exploration.
• Score: 10.60691612679966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces an innovative approach to boost the efficiency and scalability of Evolutionary Rule-based machine Learning (ERL), a key technique in explainable AI. While traditional ERL systems can distribute processes across multiple CPUs, fitness evaluation of candidate rules is a bottleneck, especially with large datasets. The method proposed in this paper, AERL (Accelerated ERL) solves this problem in two ways. First, by adopting GPU-optimized rule sets through a tensorized representation within the PyTorch framework, AERL mitigates the bottleneck and accelerates fitness evaluation significantly. Second, AERL takes further advantage of the GPUs by fine-tuning the rule coefficients via back-propagation, thereby improving search space exploration. Experimental evidence confirms that AERL search is faster and more effective, thus empowering explainable artificial intelligence.

Related papers

• Revisiting LLM Reasoning via Information Bottleneck [57.519119962528166]
  Large language models (LLMs) have recently demonstrated remarkable progress in reasoning capabilities through reinforcement learning with verifiable rewards (RLVR)<n>We present a theoretical characterization of LLM reasoning grounded in information bottleneck (IB) principle.<n>We propose IB-aware reasoning optimization (IBRO), a framework that encourages reasoning trajectories to be both informative about the final correct answer and generalizable.
  arXiv  Detail & Related papers  (2025-07-24T13:14:25Z)
• GPU Kernel Scientist: An LLM-Driven Framework for Iterative Kernel Optimization [0.18416014644193066]
  " GPU Kernel Scientist" is an automated methodology for iteratively refining accelerator kernels.<n>Our methodology employs LLMs in a multi-stage, evolutionary process.<n>We detail how this approach navigates the challenges of the AMD MI300 target architecture.
  arXiv  Detail & Related papers  (2025-06-25T19:59:34Z)
• Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network [17.24129493200616]
  This paper proposes a novel surrogate-assisted ERL that integrates Autoencoders (AE) and Hyperbolic Neural Networks (HNN)<n>AE compresses high-dimensional policies into low-dimensional representations while extracting key features as the inputs for the surrogate.<n> experiments on 10 Atari and 4 Mujoco games have verified that the proposed method outperforms previous approaches significantly.
  arXiv  Detail & Related papers  (2025-05-26T02:25:17Z)
• Effective Inference-Free Retrieval for Learned Sparse Representations [19.54810957623511]
  Learned Sparse Retrieval (LSR) is an effective IR approach that exploits pre-trained language models for encoding text into a learned bag of words.<n>Recently, new efficient -- inverted index-based -- retrieval engines have been proposed, leading to a natural question: has the role of regularization changed in training LSR models?<n>We show that regularization can be relaxed to produce more effective LSR encoders.
  arXiv  Detail & Related papers  (2025-04-30T09:10:46Z)
• Direct Advantage Regression: Aligning LLMs with Online AI Reward [59.78549819431632]
  Online AI Feedback (OAIF) presents a promising alternative to Reinforcement Learning from Human Feedback (RLHF) We propose Direct Advantage Regression (DAR) to optimize policy improvement through weighted supervised fine-tuning. Our empirical results underscore that AI reward is a better form of AI supervision consistently achieving higher human-AI agreement as opposed to AI preference.
  arXiv  Detail & Related papers  (2025-04-19T04:44:32Z)
• ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning [50.53705050673944]
  We propose ULTHO, an ultra-lightweight yet powerful framework for fast HPO in deep RL within single runs. Specifically, we formulate the HPO process as a multi-armed bandit with clustered arms (MABC) and link it directly to long-term return optimization. We test ULTHO on benchmarks including ALE, Procgen, MiniGrid, and PyBullet.
  arXiv  Detail & Related papers  (2025-03-08T07:03:43Z)
• EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning [24.389896398264202]
  We introduce $texttt$textbfEvoRL$$, the first end-to-end EvoRL framework optimized for GPU acceleration. The framework executes the entire training pipeline on accelerators, including environment simulations and EC processes.
  arXiv  Detail & Related papers  (2025-01-25T08:31:07Z)
• Search for Efficient Large Language Models [52.98684997131108]
  Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research. Weight pruning, quantization, and distillation have been embraced to compress LLMs, targeting memory reduction and inference acceleration. Most model compression techniques concentrate on weight optimization, overlooking the exploration of optimal architectures.
  arXiv  Detail & Related papers  (2024-09-25T21:32:12Z)
• AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs [57.12929098407975]
  We show that by efficiently parallelizing existing causal discovery methods, we can scale them to thousands of dimensions. Specifically, we focus on the causal ordering subprocedure in DirectLiNGAM and implement GPU kernels to accelerate it. This allows us to apply DirectLiNGAM to causal inference on large-scale gene expression data with genetic interventions yielding competitive results.
  arXiv  Detail & Related papers  (2024-03-06T15:06:11Z)
• NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications [6.873777465945062]
  We establish a new benchmark of evolutionary optimization methods (NeuroEvoBench) tailored toward Deep Learning applications. We investigate core scientific questions including resource allocation, fitness shaping, normalization, regularization & scalability of EO.
  arXiv  Detail & Related papers  (2023-11-04T12:42:38Z)
• Efficient GNN Explanation via Learning Removal-based Attribution [56.18049062940675]
  We propose a framework of GNN explanation named LeArn Removal-based Attribution (LARA) to address this problem. The explainer in LARA learns to generate removal-based attribution which enables providing explanations with high fidelity. In particular, LARA is 3.5 times faster and achieves higher fidelity than the state-of-the-art method on the large dataset ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-06-09T08:54:20Z)
• M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation [145.7321032755538]
  Learning to Optimize (L2O) has drawn increasing attention as it often remarkably accelerates the optimization procedure of complex tasks. This paper investigates a potential solution to this open challenge by meta-training an L2O that can perform fast test-time self-adaptation to an out-of-distribution task.
  arXiv  Detail & Related papers  (2023-02-28T19:23:20Z)
• Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding [28.272572839321104]
  This paper proposes a PE-SAERL Framework to enable surrogate-assisted evolutionary reinforcement learning via policy embedding. Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-01-31T02:36:06Z)
• Deep Black-Box Reinforcement Learning with Movement Primitives [15.184283143878488]
  We present a new algorithm for deep reinforcement learning (RL) It is based on differentiable trust region layers, a successful on-policy deep RL algorithm. We compare our ERL algorithm to state-of-the-art step-based algorithms in many complex simulated robotic control tasks.
  arXiv  Detail & Related papers  (2022-10-18T06:34:52Z)
• Efficient Few-Shot Object Detection via Knowledge Inheritance [62.36414544915032]
  Few-shot object detection (FSOD) aims at learning a generic detector that can adapt to unseen tasks with scarce training samples. We present an efficient pretrain-transfer framework (PTF) baseline with no computational increment. We also propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights.
  arXiv  Detail & Related papers  (2022-03-23T06:24:31Z)
• Learning to Optimize: A Primer and A Benchmark [94.29436694770953]
  Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods. This article is poised to be the first comprehensive survey and benchmark of L2O for continuous optimization.
  arXiv  Detail & Related papers  (2021-03-23T20:46:20Z)
• Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units [3.5027291542274357]
  We propose a novel framework for accelerating explainable machine learning (ML) using Processing Units (TPUs) The proposed framework exploits the synergy between matrix convolution and Fourier transform, and takes full advantage of TPU's natural ability in accelerating matrix computations. Our proposed approach is applicable across a wide variety of ML algorithms, and effective utilization of TPU-based acceleration can lead to real-time outcome interpretation.
  arXiv  Detail & Related papers  (2021-03-22T15:11:45Z)

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.