{"total":31,"rows":[{"id":"2312.03863","title":"Efficient Large Language Models: A Survey","created":"2023-12-06","cited_by_count":186,"area":null},{"id":"2401.08092","title":"A Survey of Resource-efficient LLM and Multimodal Foundation Models","created":"2024-01-16","cited_by_count":86,"area":null},{"id":"2310.00227","title":"Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement","created":"2023-09-30","cited_by_count":77,"area":null},{"id":"2302.01107","title":"A Survey on Efficient Training of Transformers","created":"2023-02-02","cited_by_count":39,"area":null},{"id":"2410.19313","title":"COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training","created":"2024-10-25","cited_by_count":33,"area":null},{"id":"2307.09988","title":"TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge","created":"2023-07-19","cited_by_count":18,"area":null},{"id":"2410.15352","title":"CompAct: Compressed Activations for Memory-Efficient LLM Training","created":"2024-10-20","cited_by_count":15,"area":null},{"id":"2403.14737","title":"FedMef: Towards Memory-efficient Federated Dynamic Pruning","created":"2024-03-21","cited_by_count":14,"area":null},{"id":"2505.01043","title":"Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities","created":"2025-05-02","cited_by_count":11,"area":null},{"id":"2305.15265","title":"Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model","created":"2023-05-24","cited_by_count":9,"area":null},{"id":"2411.13055","title":"Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training","created":"2024-11-20","cited_by_count":8,"area":null},{"id":"2402.17812","title":"DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation","created":"2024-02-27","cited_by_count":7,"area":null},{"id":"2503.19050","title":"Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization","created":"2025-03-24","cited_by_count":7,"area":null},{"id":"2412.04180","title":"SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization","created":"2024-12-05","cited_by_count":6,"area":null},{"id":"2503.01905","title":"PaCA: Partial Connection Adaptation for Efficient Fine-Tuning","created":"2025-02-28","cited_by_count":6,"area":null},{"id":"2305.18513","title":"SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics","created":"2023-05-29","cited_by_count":5,"area":null},{"id":"2406.16282","title":"Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation","created":"2024-06-24","cited_by_count":5,"area":null},{"id":"2308.00890","title":"Tango: rethinking quantization for graph neural network training on GPUs","created":"2023-08-02","cited_by_count":4,"area":null},{"id":"2506.01352","title":"TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network","created":"2025-06-02","cited_by_count":4,"area":null},{"id":"2402.17227","title":"Efficient Backpropagation with Variance-Controlled Adaptive Sampling","created":"2024-02-27","cited_by_count":3,"area":null},{"id":"2507.07149","title":"DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training","created":"2025-07-09","cited_by_count":3,"area":null},{"id":"2310.02422","title":"OneAdapt: Fast Configuration Adaptation for Video Analytics Applications via Backpropagation","created":"2023-10-03","cited_by_count":2,"area":null},{"id":"2506.02939","title":"QKV Projections Require a Fraction of Their Memory","created":"2025-06-03","cited_by_count":2,"area":null},{"id":"2510.11400","title":"FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management","created":"2025-10-13","cited_by_count":1,"area":null},{"id":"2303.14633","title":"An Evaluation of Memory Optimization Methods for Training Neural Networks","created":"2023-03-26","cited_by_count":0,"area":null},{"id":"2405.18800","title":"Face processing emerges from object-trained convolutional neural networks","created":"2024-05-29","cited_by_count":0,"area":null},{"id":"2503.13795","title":"BurTorch: Revisiting Training from First Principles by Coupling Autodiff, Math Optimization, and Systems","created":"2025-03-18","cited_by_count":0,"area":null},{"id":"2508.15033","title":"Rethinking the Potential of Layer Freezing for Efficient DNN Training","created":"2025-08-20","cited_by_count":0,"area":null},{"id":"2602.20646","title":"On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2605.00539","title":"AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2609.39813","title":"Backward-State Policy Is Part of the Learning Algorithm","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}