{"total":40,"rows":[{"id":"2102.04906","title":"Dynamic Neural Networks: A Survey","created":"2021-02-09","cited_by_count":408,"area":null},{"id":"2111.15668","title":"AdaViT: Adaptive Vision Transformers for Efficient Image Recognition","created":"2021-11-30","cited_by_count":228,"area":null},{"id":"2403.02181","title":"Not All Layers of LLMs Are Necessary During Inference","created":"2024-03-04","cited_by_count":96,"area":null},{"id":"2110.01515","title":"A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning","created":"2021-10-04","cited_by_count":82,"area":null},{"id":"2108.00803","title":"Learn to Match: Automatic Matching Network Design for Visual Tracking","created":"2021-08-02","cited_by_count":67,"area":null},{"id":"2104.13400","title":"FrameExit: Conditional Early Exiting for Efficient Video Recognition","created":"2021-04-27","cited_by_count":59,"area":null},{"id":"2306.11248","title":"Dynamic Perceiver for Efficient Visual Recognition","created":"2023-06-20","cited_by_count":33,"area":null},{"id":"2104.11487","title":"Skip-Convolutions for Efficient Video Processing","created":"2021-04-23","cited_by_count":32,"area":null},{"id":"2308.15949","title":"Latency-aware Unified Dynamic Networks for Efficient Image Recognition","created":"2023-08-30","cited_by_count":26,"area":null},{"id":"2210.06223","title":"Latency-aware Spatial-wise Dynamic Networks","created":"2022-10-12","cited_by_count":25,"area":null},{"id":"2205.03055","title":"Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism","created":"2022-05-06","cited_by_count":20,"area":null},{"id":"2307.02321","title":"MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers","created":"2023-07-05","cited_by_count":20,"area":null},{"id":"2101.08430","title":"Generative Zero-shot Network Quantization","created":"2021-01-21","cited_by_count":15,"area":null},{"id":"2306.04451","title":"Referring Expression Comprehension Using Language Adaptive Inference","created":"2023-06-06","cited_by_count":15,"area":null},{"id":"2110.03252","title":"Layer-wise Pruning of Transformer Attention Heads for Efficient Language Modeling","created":"2021-10-07","cited_by_count":14,"area":null},{"id":"2308.13998","title":"Computation-efficient Deep Learning for Computer Vision: A Survey","created":"2023-08-27","cited_by_count":14,"area":null},{"id":"2112.00492","title":"Human-Object Interaction Detection via Weak Supervision","created":"2021-12-01","cited_by_count":5,"area":null},{"id":"2205.06404","title":"Fast Conditional Network Compression Using Bayesian HyperNetworks","created":"2022-05-13","cited_by_count":5,"area":null},{"id":"2503.04963","title":"Energy-Latency Attacks: A New Adversarial Threat to Deep Learning","created":"2025-03-06","cited_by_count":5,"area":null},{"id":"2201.11103","title":"Auto-Compressing Subset Pruning for Semantic Image Segmentation","created":"2022-01-26","cited_by_count":4,"area":null},{"id":"2304.00280","title":"Progressive Channel-Shrinking Network","created":"2023-04-01","cited_by_count":4,"area":null},{"id":"2309.17211","title":"Data-Free Dynamic Compression of CNNs for Tractable Efficiency","created":"2023-09-29","cited_by_count":4,"area":null},{"id":"2303.05078","title":"Efficient Transformer-based 3D Object Detection with Dynamic Token Halting","created":"2023-03-09","cited_by_count":3,"area":null},{"id":"2310.02544","title":"SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers","created":"2023-10-04","cited_by_count":3,"area":null},{"id":"2409.17016","title":"CNN Mixture-of-Depths","created":"2024-09-25","cited_by_count":3,"area":null},{"id":"2306.16869","title":"NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes","created":"2023-06-29","cited_by_count":2,"area":null},{"id":"2109.05432","title":"Prioritized Subnet Sampling for Resource-Adaptive Supernet Training","created":"2021-09-12","cited_by_count":1,"area":null},{"id":"2302.10798","title":"Learning a Consensus Sub-Network with Polarization Regularization and One Pass Training","created":"2023-02-17","cited_by_count":1,"area":null},{"id":"2310.02776","title":"Dynamic Shuffle: An Efficient Channel Mixture Method","created":"2023-10-04","cited_by_count":1,"area":null},{"id":"2403.05050","title":"DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomous Driving Streaming Perception","created":"2024-03-08","cited_by_count":1,"area":null},{"id":"2111.14302","title":"Self-supervised Feature-Gate Coupling for Dynamic Network Pruning","created":"2021-11-29","cited_by_count":0,"area":null},{"id":"2202.05343","title":"Towards Disentangling Information Paths with Coded ResNeXt","created":"2022-02-10","cited_by_count":0,"area":null},{"id":"2205.15404","title":"Gator: Customizable Channel Pruning of Neural Networks with Gating","created":"2022-05-30","cited_by_count":0,"area":null},{"id":"2206.12420","title":"SCAI: A Spectral data Classification framework with Adaptive Inference for the IoT platform","created":"2022-06-24","cited_by_count":0,"area":null},{"id":"2301.09164","title":"Unifying Synergies between Self-supervised Learning and Dynamic Computation","created":"2023-01-22","cited_by_count":0,"area":null},{"id":"2303.14772","title":"$\\Delta$-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss","created":"2023-03-26","cited_by_count":0,"area":null},{"id":"2305.08021","title":"TIPS: Topologically Important Path Sampling for Anytime Neural Networks","created":"2023-05-13","cited_by_count":0,"area":null},{"id":"2603.12354","title":"Alternating Gradient Flow Utility: A Unified Metric for Structural Pruning and Dynamic Routing in Deep Networks","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2606.30516","title":"HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2608.04811","title":"StaticSegFormer: An Efficient High-Performance Semantic Segmentation Based on Static Structured Pruning","created":"2026-08-05","cited_by_count":0,"area":null}],"next_cursor":null}