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Collections including paper arxiv:2309.10713

Woodpecker: Hallucination Correction for Multimodal Large Language Models

Paper • 2310.16045 • Published Oct 24, 2023 • 17
HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models

Paper • 2310.14566 • Published Oct 23, 2023 • 27
SILC: Improving Vision Language Pretraining with Self-Distillation

Paper • 2310.13355 • Published Oct 20, 2023 • 9
Conditional Diffusion Distillation

Paper • 2310.01407 • Published Oct 2, 2023 • 20

Trellis Networks for Sequence Modeling

Paper • 1810.06682 • Published Oct 15, 2018 • 1
Pruning Very Deep Neural Network Channels for Efficient Inference

Paper • 2211.08339 • Published Nov 14, 2022 • 1
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch

Paper • 2309.14157 • Published Sep 25, 2023 • 1
Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 148

Replacing softmax with ReLU in Vision Transformers

Paper • 2309.08586 • Published Sep 15, 2023 • 18
Softmax Bias Correction for Quantized Generative Models

Paper • 2309.01729 • Published Sep 4, 2023 • 1
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression

Paper • 2304.13276 • Published Apr 26, 2023 • 1
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Paper • 2306.12929 • Published Jun 22, 2023 • 12

Unified Normalization for Accelerating and Stabilizing Transformers

Paper • 2208.01313 • Published Aug 2, 2022 • 1
Interpret Vision Transformers as ConvNets with Dynamic Convolutions

Paper • 2309.10713 • Published Sep 19, 2023 • 1
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs

Paper • 2003.00152 • Published Feb 29, 2020 • 1
SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization

Paper • 2405.11582 • Published May 19, 2024 • 17

LLM architecture

The Impact of Depth and Width on Transformer Language Model Generalization

Paper • 2310.19956 • Published Oct 30, 2023 • 10
Retentive Network: A Successor to Transformer for Large Language Models

Paper • 2307.08621 • Published Jul 17, 2023 • 172
RWKV: Reinventing RNNs for the Transformer Era

Paper • 2305.13048 • Published May 22, 2023 • 20
Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 108

Woodpecker: Hallucination Correction for Multimodal Large Language Models

Paper • 2310.16045 • Published Oct 24, 2023 • 17
HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models

Paper • 2310.14566 • Published Oct 23, 2023 • 27
SILC: Improving Vision Language Pretraining with Self-Distillation

Paper • 2310.13355 • Published Oct 20, 2023 • 9
Conditional Diffusion Distillation

Paper • 2310.01407 • Published Oct 2, 2023 • 20

Unified Normalization for Accelerating and Stabilizing Transformers

Paper • 2208.01313 • Published Aug 2, 2022 • 1
Interpret Vision Transformers as ConvNets with Dynamic Convolutions

Paper • 2309.10713 • Published Sep 19, 2023 • 1
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs

Paper • 2003.00152 • Published Feb 29, 2020 • 1
SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization

Paper • 2405.11582 • Published May 19, 2024 • 17

Trellis Networks for Sequence Modeling

Paper • 1810.06682 • Published Oct 15, 2018 • 1
Pruning Very Deep Neural Network Channels for Efficient Inference

Paper • 2211.08339 • Published Nov 14, 2022 • 1
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch

Paper • 2309.14157 • Published Sep 25, 2023 • 1
Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 148

LLM architecture

The Impact of Depth and Width on Transformer Language Model Generalization

Paper • 2310.19956 • Published Oct 30, 2023 • 10
Retentive Network: A Successor to Transformer for Large Language Models

Paper • 2307.08621 • Published Jul 17, 2023 • 172
RWKV: Reinventing RNNs for the Transformer Era

Paper • 2305.13048 • Published May 22, 2023 • 20
Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 108

Replacing softmax with ReLU in Vision Transformers

Paper • 2309.08586 • Published Sep 15, 2023 • 18
Softmax Bias Correction for Quantized Generative Models

Paper • 2309.01729 • Published Sep 4, 2023 • 1
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression

Paper • 2304.13276 • Published Apr 26, 2023 • 1
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Paper • 2306.12929 • Published Jun 22, 2023 • 12

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