{"total":30,"rows":[{"id":"2211.17192","title":"Fast Inference from Transformers via Speculative Decoding","created":"2022-11-30","cited_by_count":1653,"area":"inference"},{"id":"2207.07061","title":"Confident Adaptive Language Modeling","created":"2022-07-14","cited_by_count":310,"area":"inference"},{"id":"2402.16363","title":"LLM Inference Unveiled: Survey and Roofline Model Insights","created":"2024-02-26","cited_by_count":152,"area":"inference"},{"id":"2106.05022","title":"Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions","created":"2021-06-09","cited_by_count":75,"area":"inference"},{"id":"2312.04916","title":"EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism","created":"2023-12-08","cited_by_count":69,"area":"inference"},{"id":"2203.01670","title":"A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation","created":"2022-03-03","cited_by_count":32,"area":"inference"},{"id":"2105.13878","title":"Accelerating BERT Inference for Sequence Labeling via Early-Exit","created":"2021-05-28","cited_by_count":29,"area":"inference"},{"id":"2111.00230","title":"Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning","created":"2021-10-30","cited_by_count":16,"area":"inference"},{"id":"2306.02652","title":"Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity","created":"2023-06-05","cited_by_count":16,"area":"inference"},{"id":"2312.05385","title":"Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving","created":"2023-12-08","cited_by_count":15,"area":"inference"},{"id":"2107.00175","title":"Elbert: Fast Albert with Confidence-Window Based Early Exit","created":"2021-07-01","cited_by_count":14,"area":"inference"},{"id":"2204.06328","title":"HuBERT-EE: Early Exiting HuBERT for Efficient Speech Recognition","created":"2022-04-13","cited_by_count":13,"area":"inference"},{"id":"2312.11882","title":"ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Models Inference","created":"2023-12-19","cited_by_count":13,"area":"inference"},{"id":"2402.00518","title":"EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models","created":"2024-02-01","cited_by_count":13,"area":"inference"},{"id":"2405.15039","title":"CEEBERT: Cross-Domain Inference in Early Exit BERT","created":"2024-05-23","cited_by_count":12,"area":"inference"},{"id":"2303.09266","title":"SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference","created":"2023-03-16","cited_by_count":9,"area":"inference"},{"id":"2311.08623","title":"DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models","created":"2023-11-15","cited_by_count":7,"area":"inference"},{"id":"2403.14919","title":"Hierarchical Skip Decoding for Efficient Autoregressive Text Generation","created":"2024-03-22","cited_by_count":7,"area":"inference"},{"id":"2502.00745","title":"BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts","created":"2025-02-02","cited_by_count":6,"area":"inference"},{"id":"2306.02307","title":"Finding the SWEET Spot: Analysis and Improvement of Adaptive Inference in Low Resource Settings","created":"2023-06-04","cited_by_count":5,"area":"inference"},{"id":"2407.20272","title":"An Efficient Inference Framework for Early-exit Large Language Models","created":"2024-07-25","cited_by_count":5,"area":"inference"},{"id":"2402.05948","title":"DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks","created":"2024-02-03","cited_by_count":3,"area":"inference"},{"id":"2509.23666","title":"Beyond Greedy Exits: Improved Early Exit Decisions for Risk Control and Reliability","created":"2025-09-28","cited_by_count":3,"area":"inference"},{"id":"2506.17249","title":"Improving Prediction Certainty Estimation for Reliable Early Exiting via Null Space Projection","created":"2025-06-08","cited_by_count":2,"area":"inference"},{"id":"2206.09029","title":"Binary Early-Exit Network for Adaptive Inference on Low-Resource Devices","created":"2022-06-17","cited_by_count":1,"area":"inference"},{"id":"2409.14091","title":"Normalized Narrow Jump To Conclusions: Normalized Narrow Shortcuts for Parameter Efficient Early Exit Transformer Prediction","created":"2024-09-21","cited_by_count":1,"area":"inference"},{"id":"2412.13236","title":"COSEE: Consistency-Oriented Signal-Based Early Exiting via Calibrated Sample Weighting Mechanism","created":"2024-12-17","cited_by_count":1,"area":"inference"},{"id":"2504.13984","title":"One Jump Is All You Need: Short-Cutting Transformers for Early Exit Prediction with One Jump to Fit All Exit Levels","created":"2025-04-18","cited_by_count":0,"area":"inference"},{"id":"2604.18592","title":"Two-dimensional early exit optimisation of LLM inference","created":"2026-03-27","cited_by_count":0,"area":"inference"},{"id":"2609.32898","title":"When Less Compute Is More: Adaptive Early Exit Improves Pretrained Outlier Detection","created":"2026-09-26","cited_by_count":0,"area":"inference"}],"next_cursor":null}