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Found 7 papers, 0 papers with code
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation
In this work, we improve upon the inference latency of the state-of-the-art methods by removing the floating-point operations, which are associated with the GELU activation in Swin Transformer.
AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation
This paper describes a simple yet effective technique for refining a pretrained classifier network.
Robustness to distribution shifts of compressed networks for edge devices
It is important to investigate the robustness of compressed networks in two types of data distribution shifts: domain shifts and adversarial perturbations.
CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices.
Training Linear Finite-State Machines
Therefore, our FSM-based model can learn extremely long-term dependencies as it requires 1/l memory storage during training compared to LSTMs, where l is the number of time steps.
Decoding Polar Codes with Reinforcement Learning
In this paper we address the problem of selecting factor-graph permutations of polar codes under belief propagation (BP) decoding to significantly improve the error-correction performance of the code.
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic
The emergence of XNOR networks seek to reduce the model size and computational cost of neural networks for their deployment on specialized hardware requiring real-time processes with limited hardware resources.
