4 repositorios
Awesome GitHub RepositoriesSelective State Space Models
Sequence models that utilize data-dependent state gating to achieve linear-time complexity.
Distinct from Sequence Models: Focuses on selective state space architectures specifically, distinct from general sequence models.
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Awesome Selective State Space Models GitHub Repositories
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- state-spaces/mamba
state-spaces/mamba
17,215Ver en GitHubMamba is a deep learning framework designed for building and training sequence models that process long-range data dependencies with linear-time computational efficiency. By utilizing selective state space modeling, the library enables the construction of neural network architectures that replace traditional attention mechanisms with high-performance state space operations. The framework distinguishes itself through the use of data-dependent state gating, which allows the model to dynamically filter information flow based on the input sequence. To ensure high throughput, it incorporates hardw
Implements selective state space modeling to process long-range dependencies with linear-time efficiency.
Python - shangtongzhang/reinforcement-learning-an-introduction
ShangtongZhang/reinforcement-learning-an-introduction
14,569Ver en GitHubThis project is a Python-based educational framework designed to simulate reinforcement learning algorithms and environments. It serves as a platform for reproducing classic textbook examples, allowing users to study agent behavior, policy improvement, and the fundamental mechanics of decision-making in controlled settings. The library provides implementations for core reinforcement learning concepts, including temporal difference learning, Monte Carlo episode sampling, and tabular value function approximation. It enables the analysis of specific algorithmic behaviors, such as identifying and
Provides discrete state-space modeling for exact value function calculation in reinforcement learning environments.
Pythonartificial-intelligencereinforcement-learning - fla-org/flash-linear-attention
fla-org/flash-linear-attention
5,248Ver en GitHubFlash Linear Attention is a training framework and inference engine for sequence models that use linear attention and state space mechanisms, designed to process long contexts with reduced memory and compute overhead. It provides hardware-optimized token mixing layers and fused CUDA kernels that minimize memory bandwidth and launch overhead across different GPU architectures, and includes a causal inference engine that generates text token-by-token using cached hidden states for efficient autoregressive decoding. The project supports building hybrid sequence models that interleave standard at
Processes sequences using recurrent state updates that capture long-range dependencies with linear complexity.
Pythonlarge-language-modelsmachine-learning-systemsnatural-language-processing - rlabbe/filterpy
rlabbe/filterpy
3,772Ver en GitHubfilterpy is a toolkit for Bayesian state estimation, Gaussian statistical analysis, and time-series noise reduction. It provides a library of linear and non-linear Kalman filters, as well as routines for non-Gaussian state estimation and signal smoothing. The project implements a variety of estimation methods, including particle filtering using Markov Chain Monte Carlo and resampling, and discrete Bayes filtering. It also includes a suite of algorithms for refining historical state estimates through backward and fixed-lag smoothing. Additional capabilities cover multivariate Gaussian analysi
Calculates probability distributions over a discrete set of states to identify the most likely current state.
Python
