Linear Transformers with Learnable Kernel Functions are Better In-Context Models
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2402.10644
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Published
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79
Papers - Attention
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GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Paper • 2305.13245 • Published • 5 -
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition
Paper • 2402.15220 • Published • 19 -
Sequence Parallelism: Long Sequence Training from System Perspective
Paper • 2105.13120 • Published • 5 -
Ring Attention with Blockwise Transformers for Near-Infinite Context
Paper • 2310.01889 • Published • 10 -
Striped Attention: Faster Ring Attention for Causal Transformers
Paper • 2311.09431 • Published • 4 -
Longformer: The Long-Document Transformer
Paper • 2004.05150 • Published • 3 -
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Paper • 2006.03654 • Published • 3 -
DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
Paper • 2111.09543 • Published • 2 -
Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes
Paper • 2110.05909 • Published • 2 -
3D Medical Image Segmentation based on multi-scale MPU-Net
Paper • 2307.05799 • Published • 2 -
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation
Paper • 2210.16898 • Published • 2 -
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
Paper • 2107.00652 • Published • 2 -
BOAT: Bilateral Local Attention Vision Transformer
Paper • 2201.13027 • Published • 2 -
MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition
Paper • 2209.01620 • Published • 2 -
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Paper • 2103.14030 • Published • 4 -
Using Multi-scale SwinTransformer-HTC with Data augmentation in CoNIC Challenge
Paper • 2202.13588 • Published • 2 -
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
Paper • 2211.00593 • Published • 2 -
BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
Paper • 2403.09347 • Published • 20 -
Vision Transformer with Quadrangle Attention
Paper • 2303.15105 • Published • 2 -
Lightweight Image Inpainting by Stripe Window Transformer with Joint Attention to CNN
Paper • 2301.00553 • Published • 2 -
Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers
Paper • 2311.10642 • Published • 23 -
Code Completion using Neural Attention and Byte Pair Encoding
Paper • 2004.06343 • Published • 2 -
Recurrent Drafter for Fast Speculative Decoding in Large Language Models
Paper • 2403.09919 • Published • 20 -
Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers
Paper • 2403.12943 • Published • 14 -
VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis
Paper • 2403.13501 • Published • 9 -
Efficient Memory Management for Large Language Model Serving with PagedAttention
Paper • 2309.06180 • Published • 25 -
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
Paper • 2404.07143 • Published • 103 -
Sparse Laneformer
Paper • 2404.07821 • Published • 11 -
JetMoE: Reaching Llama2 Performance with 0.1M Dollars
Paper • 2404.07413 • Published • 36 -
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
Paper • 2404.08801 • Published • 63 -
Hydragen: High-Throughput LLM Inference with Shared Prefixes
Paper • 2402.05099 • Published • 19 -
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Paper • 2402.15627 • Published • 34 -
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation
Paper • 2404.11565 • Published • 14 -
SpecInfer: Accelerating Generative LLM Serving with Speculative Inference and Token Tree Verification
Paper • 2305.09781 • Published • 4 -
GLIGEN: Open-Set Grounded Text-to-Image Generation
Paper • 2301.07093 • Published • 3 -
FlashSpeech: Efficient Zero-Shot Speech Synthesis
Paper • 2404.14700 • Published • 29 -
Multi-Head Mixture-of-Experts
Paper • 2404.15045 • Published • 59 -
Transformers Can Represent n-gram Language Models
Paper • 2404.14994 • Published • 18 -
BASS: Batched Attention-optimized Speculative Sampling
Paper • 2404.15778 • Published • 8 -
InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation
Paper • 2404.19427 • Published • 71 -
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Paper • 2404.07129 • Published • 3 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 63 -
VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces
Paper • 2211.15775 • Published • 1 -
Reasoning in Large Language Models: A Geometric Perspective
Paper • 2407.02678 • Published • 1 -
Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures
Paper • 2407.09468 • Published • 1 -
Steerable Transformers
Paper • 2405.15932 • Published • 1 -
Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters
Paper • 2408.04093 • Published • 4 -
Attention Heads of Large Language Models: A Survey
Paper • 2409.03752 • Published • 88 -
Differential Transformer
Paper • 2410.05258 • Published • 166 -
ThunderKittens: Simple, Fast, and Adorable AI Kernels
Paper • 2410.20399 • Published • 1 -
HAT: Hybrid Attention Transformer for Image Restoration
Paper • 2309.05239 • Published • 1 -
Unraveling the Gradient Descent Dynamics of Transformers
Paper • 2411.07538 • Published • 2