A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Kai Han, Yunhe Wang, Hanting Chen, et al.
A comprehensive survey of vision transformer models, categorizing them by tasks, analyzing advantages and disadvantages, and discussing challenges and future directions.
Bryan Y. Lim, Sercan Ö. Arık, Nicolas Loeff, et al.
Introduces the Temporal Fusion Transformer, an attention-based architecture for interpretable multi-horizon time series forecasting that combines high performance with insights into temporal dynamics.
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Viewformer is a NeRF-free neural rendering method that uses transformers to synthesize novel views from few sparsely covering context images.
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Q-dit introduces a post-training quantization method for diffusion transformers that reduces memory and latency while maintaining generation quality.
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Lazydit introduces a lazy learning approach to accelerate Diffusion Transformers by selectively skipping redundant computations during inference.
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The paper argues that semantically richer latent representations can substantially improve diffusion Transformer optimization efficiency and expand the reconstruction–generation Pareto frontier.
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A statistical and approximation theory framework predicting neural scaling laws for transformers on intrinsically low-dimensional data, quantifying data and model scaling exponents.
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IRIS introduces a Transformer-based world model that learns from pixels and achieves human-level performance on Atari 100k with high sample efficiency.
Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li, et al.
Proposes DeepSC, a Transformer-based semantic communication system for text transmission that maximizes system capacity and minimizes semantic errors, with transfer learning and a new sentence similarity metric.
Giorgio Roffo
This tutorial explores advanced LLM techniques including RAG, PAL, ReAct, LangChain, fine-tuning strategies, and transformer architectures to address limitations like temporal cutoffs and mathematical inaccuracies.
Jos'ephine Raugel, Marc Szafraniec, Huy V. Vo, et al.
This paper systematically varies model size, training amount, and image type in DINOv3 vision transformers to disentangle how these factors independently and interactively drive brain-like representations, revealing a developmental chronology aligned
Qi Sun, Edoardo Cetin, Yujin Tang
Transformer-Squared is a self-adaptive LLM framework that adjusts only the singular components of weight matrices in real-time, using a two-pass dispatch and reinforcement-learned expert vectors to outperform LoRA with fewer parameters.