惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

GbyAI
GbyAI
Y
Y Combinator Blog
F
Fortinet All Blogs
H
Hackread – Cybersecurity News, Data Breaches, AI and More
N
Netflix TechBlog - Medium
T
Tailwind CSS Blog
aimingoo的专栏
aimingoo的专栏
博客园 - Franky
T
The Blog of Author Tim Ferriss
D
DataBreaches.Net
量子位
博客园 - 三生石上(FineUI控件)
I
InfoQ
Engineering at Meta
Engineering at Meta
WordPress大学
WordPress大学
阮一峰的网络日志
阮一峰的网络日志
爱范儿
爱范儿
D
Docker
美团技术团队
雷峰网
雷峰网
U
Unit 42
Stack Overflow Blog
Stack Overflow Blog
Recent Announcements
Recent Announcements
人人都是产品经理
人人都是产品经理

cs.LG updates on arXiv.org

Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning Reservoir observer enhanced with residual calibration and attention mechanism Efficient RL Training for LLMs with Experience Replay Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing Adaptive Simulation Experiment for LLM Policy Optimization EvoLen: Evolution-Guided Tokenization for DNA Language Model Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis Loom: A Scalable Analytical Neural Computer Architecture Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds How does Chain of Thought decompose complex tasks? Uncertainty-Aware Transformers: Conformal Prediction for Language Models Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization Using Synthetic Data for Machine Learning-based Childhood Vaccination Prediction in Narok, Kenya Delve into the Applicability of Advanced Optimizers for Multi-Task Learning Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning Multi-Agent Decision-Focused Learning via Value-Aware Sequential Communication Predictive Entropy Links Calibration and Paraphrase Sensitivity in Medical Vision-Language Models Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge Feature-Label Modal Alignment for Robust Partial Multi-Label Learning Integrated electro-optic attention nonlinearities for transformers Toward World Models for Epidemiology Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
Graceful forgetting: Memory as a process
Alain de Cheveigné · 2025-02-16 · via cs.LG updates on arXiv.org

A rational framework is proposed to explain how we accommodate unbounded sensory input within bounded memory. According to this framework, memory is stored as a statistic-like representation that is repeatedly summarized and compressed to make room for new input. Summarization of sensory input must be rapid; that of abstract trace might be slower and more deliberative, drawing on elaborative processes some of which might occasionally reach consciousness (as in mind-wandering). Short-term sensory traces are summarized as simple statistics organized into structures such as a time series, graph or dictionary, and longer-term abstract traces as more complex statistic-like structures. Summarization at multiple time scales requires an intensive process of memory curation which might account for the high metabolic consumption of the brain at rest. Summarization may be guided by heuristics to help choose which statistics to apply at each step, so that the trace is useful for a wide range of future needs, the objective being to "represent the past" rather than tune for a specific task. However, the choice of statistics (or of heuristics to guide that choice) is a potential target for learning, possibly over long-term scales of development or evolution. The framework is intended as an aid to make sense of our extensive empirical and theoretical knowledge of memory and bring us closer to understanding it in functional and mechanistic terms.