{"total":55,"rows":[{"id":"2211.05102","title":"Efficiently Scaling Transformer Inference","created":"2022-11-09","cited_by_count":539,"area":null},{"id":"2401.15024","title":"SliceGPT: Compress Large Language Models by Deleting Rows and Columns","created":"2024-01-26","cited_by_count":385,"area":null},{"id":"2404.02258","title":"Mixture-of-Depths: Dynamically allocating compute in transformer-based language models","created":"2024-04-02","cited_by_count":258,"area":null},{"id":"2108.05542","title":"AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing","created":"2021-08-12","cited_by_count":131,"area":null},{"id":"2309.12314","title":"TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance","created":"2023-09-21","cited_by_count":90,"area":null},{"id":"2111.12763","title":"Sparse is Enough in Scaling Transformers","created":"2021-11-24","cited_by_count":86,"area":null},{"id":"2205.07877","title":"A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification","created":"2022-05-14","cited_by_count":65,"area":null},{"id":"2408.09632","title":"MoDeGPT: Modular Decomposition for Large Language Model Compression","created":"2024-08-19","cited_by_count":48,"area":null},{"id":"2310.19956","title":"The Impact of Depth on Compositional Generalization in Transformer Language Models","created":"2023-10-30","cited_by_count":38,"area":null},{"id":"2404.07236","title":"Lightweight Deep Learning for Resource-Constrained Environments: A Survey","created":"2024-04-08","cited_by_count":38,"area":null},{"id":"2201.08542","title":"Can Model Compression Improve NLP Fairness","created":"2022-01-21","cited_by_count":20,"area":null},{"id":"2406.16330","title":"Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging","created":"2024-06-24","cited_by_count":19,"area":null},{"id":"2110.08460","title":"A Short Study on Compressing Decoder-Based Language Models","created":"2021-10-16","cited_by_count":16,"area":null},{"id":"2401.15347","title":"A Comprehensive Survey of Compression Algorithms for Language Models","created":"2024-01-27","cited_by_count":15,"area":null},{"id":"2310.17903","title":"Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey","created":"2023-10-27","cited_by_count":9,"area":null},{"id":"2203.00748","title":"E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models","created":"2022-03-01","cited_by_count":8,"area":null},{"id":"2507.04636","title":"Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework","created":"2025-07-07","cited_by_count":8,"area":null},{"id":"2109.11105","title":"Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing","created":"2021-09-23","cited_by_count":7,"area":null},{"id":"2310.10054","title":"NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models","created":"2023-10-16","cited_by_count":7,"area":null},{"id":"2110.15225","title":"Pruning Attention Heads of Transformer Models Using A* Search: A Novel Approach to Compress Big NLP Architectures","created":"2021-10-28","cited_by_count":6,"area":null},{"id":"2211.04256","title":"Bridging Fairness and Environmental Sustainability in Natural Language Processing","created":"2022-11-08","cited_by_count":5,"area":null},{"id":"2405.00632","title":"When Quantization Affects Confidence of Large Language Models?","created":"2024-05-01","cited_by_count":5,"area":null},{"id":"2407.13331","title":"Reconstruct the Pruned Model without Any Retraining","created":"2024-07-18","cited_by_count":4,"area":null},{"id":"2105.01064","title":"Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems","created":"2021-05-04","cited_by_count":3,"area":null},{"id":"2401.06495","title":"An investigation of structures responsible for gender bias in BERT and DistilBERT","created":"2024-01-12","cited_by_count":3,"area":null},{"id":"2403.13901","title":"Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases","created":"2024-03-20","cited_by_count":3,"area":null},{"id":"2207.10702","title":"Efficient model compression with Random Operation Access Specific Tile (ROAST) hashing","created":"2022-07-21","cited_by_count":2,"area":null},{"id":"2305.06404","title":"LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM","created":"2023-05-10","cited_by_count":2,"area":null},{"id":"2401.07456","title":"Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation","created":"2024-01-15","cited_by_count":2,"area":null},{"id":"2405.06038","title":"From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks","created":"2024-05-09","cited_by_count":2,"area":null},{"id":"2407.13174","title":"Compressed models are NOT miniature versions of large models","created":"2024-07-18","cited_by_count":2,"area":null},{"id":"2602.08858","title":"FlattenGPT: Depth Compression for Transformer with Layer Flattening","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2102.02705","title":"EFloat: Entropy-coded Floating Point Format for Compressing Vector Embedding Models","created":"2021-02-04","cited_by_count":1,"area":null},{"id":"2201.03327","title":"Latency Adjustable Transformer Encoder for Language Understanding","created":"2022-01-10","cited_by_count":1,"area":null},{"id":"2305.14919","title":"Frugal Prompting for Dialog Models","created":"2023-05-24","cited_by_count":1,"area":null},{"id":"2308.08234","title":"Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Survey","created":"2023-08-16","cited_by_count":1,"area":null},{"id":"2310.15724","title":"Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules","created":"2023-10-24","cited_by_count":1,"area":null},{"id":"2311.17943","title":"LayerCollapse: Adaptive compression of neural networks","created":"2023-11-29","cited_by_count":1,"area":null},{"id":"2312.10702","title":"Can persistent homology whiten Transformer-based black-box models? A case study on BERT compression","created":"2023-12-17","cited_by_count":1,"area":null},{"id":"2402.04012","title":"Quantized Approximately Orthogonal Recurrent Neural Networks","created":"2024-02-05","cited_by_count":1,"area":null},{"id":"2408.08250","title":"Computer Vision Model Compression Techniques for Embedded Systems: A Survey","created":"2024-08-15","cited_by_count":1,"area":null},{"id":"2507.05940","title":"Chat-Ghosting: A Comparative Study of Methods for Auto-Completion in Dialog Systems","created":"2025-07-08","cited_by_count":1,"area":null},{"id":"2209.03771","title":"Stochastic gradient descent with gradient estimator for categorical features","created":"2022-09-08","cited_by_count":0,"area":null},{"id":"2309.12202","title":"Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023","created":"2023-09-14","cited_by_count":0,"area":null},{"id":"2311.11452","title":"Physics-Enhanced TinyML for Real-Time Detection of Ground Magnetic Anomalies","created":"2023-11-19","cited_by_count":0,"area":null},{"id":"2409.05368","title":"Application Specific Compression of Deep Learning Models","created":"2024-09-09","cited_by_count":0,"area":null},{"id":"2503.03140","title":"Task-Agnostic Federation over Decentralized Data: Research Landscape and Visions","created":"2025-03-05","cited_by_count":0,"area":null},{"id":"2503.21714","title":"As easy as PIE: understanding when pruning causes language models to disagree","created":"2025-03-27","cited_by_count":0,"area":null},{"id":"2508.13533","title":"Models in the Same Family are NOT Trust-Equivalent","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2510.06125","title":"Downsized and Compromised?: Assessing the Faithfulness of Model Compression","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2602.07547","title":"Linguistic properties and model scale in brain encoding: from small to compressed language models","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2604.02659","title":"Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration","created":"2026-04-03","cited_by_count":0,"area":null},{"id":"2605.08885","title":"Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2606.00130","title":"Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Parameter Compression of Deep Neural Networks","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2607.18284","title":"Compressing What Matters: Neuron Importance Meets Data-Aware Low Rank Approximation for Language Model Compression","created":"2026-06-30","cited_by_count":0,"area":null}],"next_cursor":null}