XDA Developers on MSN

6 local LLMs I've used that prove they're not just smaller versions of cloud models

These local LLMs are changing the game in lots of fun ways.
IEEE

DiGIT: Multi-Dilated Gated Encoder and Central-Adjacent Region Integrated Decoder for Temporal Action Detection Transformer

Abstract: In this paper, we examine a key limitation in query-based detectors for temporal action detection (TAD), which arises from their direct adaptation of originally designed architectures for ...
IEEE

ERD: Encoder-Residual-Decoder Neural Network for Underwater Image Enhancement

Abstract: In underwater environments, the absorption and scattering of light often result in various types of degradation in captured images, including color cast, low contrast, low brightness, and ...
GitHub

HanhaiNotHai/attention-is-all-you-need-build-the-transformer-from-scratch

Reimplement the original encoder-decoder Transformer end to end in PyTorch, from token vocabularies and sinusoidal positional encodings through multi-head attention, label smoothing, Noam scheduling, ...
GitHub

randomwish/attention-is-all-you-need-build-the-transformer-from-scratch

Reimplement the original encoder-decoder Transformer end to end in PyTorch, from token vocabularies and sinusoidal positional encodings through multi-head attention, label smoothing, Noam scheduling, ...
Frontiers

GAESA-iFormer: A Graph Attention-Enhanced Scale-Aware Inverted Transformer for Performance Characterization of Aircraft Engine Compressors

To address this challenge, this paper proposes the Graph Attention Enhanced Scale-Aware Inverted Transformer (GAESA-iFormer), a novel surrogate modeling framework integrating a Graph Attention Network ...