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Resource-efficient Algorithms and Systems of Foundation Models: A Survey
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User-Aware Prefix-Tuning Is a Good Learner for Personalized Image Captioning
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LegoDroid: flexible Android app decomposition and instant installation
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Masked Autoencoders Are Scalable Vision Learners
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LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation
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FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
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ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs
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DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale
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EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices
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Competition-level code generation with AlphaCode
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A First Look at Deep Learning Apps on Smartphones
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Proceedings of the ACM Web Conference 2022
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Mobius: Fine Tuning Large-Scale Models on Commodity GPU Servers
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OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization
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A Practical Survey on Faster and Lighter Transformers
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GEMINI: Fast Failure Recovery in Distributed Training with In-Memory Checkpoints
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Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates
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SpotServe: Serving Generative Large Language Models on Preemptible Instances
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EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
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FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs
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Mobile Foundation Model as Firmware
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AutoDroid: LLM-powered Task Automation in Android
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Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
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The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
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Diagnostic and Prognostic Value of Protein Post-Translational Modifications in Hepatocellular Carcinoma
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PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
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Token Mixing: Parameter-Efficient Transfer Learning from Image-Language to Video-Language
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OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
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DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference
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Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech
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MATE: Multi-view Attention for Table Transformer Efficiency
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Block Pruning For Faster Transformers
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Efficient Attentions for Long Document Summarization
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Compression of Generative Pre-trained Language Models via Quantization
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Meta-learning via Language Model In-context Tuning
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Deduplicating Training Data Makes Language Models Better
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Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
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TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
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AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
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ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts
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MoEfication: Transformer Feed-forward Layers are Mixtures of Experts
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Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models
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Knowledge Inheritance for Pre-trained Language Models
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Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization
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Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Sequence Parallelism: Long Sequence Training from System Perspective
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Parameter-Efficient Fine-Tuning without Introducing New Latency
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Parallel Context Windows for Large Language Models
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Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty
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Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
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Adapting Language Models to Compress Contexts
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Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding
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Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
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LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
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Domain Aligned Prefix Averaging for Domain Generalization in Abstractive Summarization
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Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
inferredcites →https://doi.org/10.18653/v1/2023.findings-acl.659openalex-snapshot
PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation
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MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
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Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Proceedings of the 31st annual meeting on Association for Computational Linguistics -
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Backprop with Approximate Activations for Memory-efficient Network Training
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BERT Loses Patience: Fast and Robust Inference with Early Exit
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Efficient Attentions for Long Document Summarization
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DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
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Sustainable AI: Environmental Implications, Challenges and Opportunities
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Mesa: A Memory-saving Training Framework for Transformers
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BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence
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Pseudo Numerical Methods for Diffusion Models on Manifolds
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MiCS: Near-linear Scaling for Training Gigantic Model on Public Cloud
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AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
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GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
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PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices
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MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
inferredcites →https://doi.org/10.48550/arxiv.2212.10670openalex-snapshot
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models
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UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
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Accelerating Large Language Model Decoding with Speculative Sampling
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Oscillation-free Quantization for Low-bit Vision Transformers
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Hyena Hierarchy: Towards Larger Convolutional Language Models
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Full Stack Optimization of Transformer Inference: a Survey
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Resurrecting Recurrent Neural Networks for Long Sequences
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Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
inferredcites →https://doi.org/10.48550/arxiv.2305.05920openalex-snapshot
Fast Distributed Inference Serving for Large Language Models
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Landmark Attention: Random-Access Infinite Context Length for Transformers
inferredcites →https://doi.org/10.48550/arxiv.2305.17888openalex-snapshot
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
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FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
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AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
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SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
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One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning
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LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation
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A Simple and Effective Pruning Approach for Large Language Models
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TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition
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SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
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Retentive Network: A Successor to Transformer for Large Language Models
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FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
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Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions
inferredcites →https://doi.org/10.48550/arxiv.2307.13304openalex-snapshot
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
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SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills
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LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression
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QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
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Efficient Streaming Language Models with Attention Sinks
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PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels
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Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
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ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Models
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