Note that to attend the event, a registration on the ICLR website is required. All workshop events (except Poster session and open discussion) can be followed using the ICLR link or use the zoom link by clicking on “join zoom” on the ICLR link.
The table below reports accepted papers and their id. Note that those ids are used to identify corresponding posters in the poster session using Gather.town link.
|1||Title: GradMax: Gradient Maximizing Neural Network Growth ggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggg|
|2||GRADIENT MATCHING FOR EFFICIENT LEARNING|
|3||FULLY QUANTIZING TRANSFORMER-BASED ASR FOR EDGE DEPLOYMENT|
|4||ActorQ: Quantization for Actor-Learner Distributed Reinforcement Learning|
|5||Optimizer Fusion: Efficient Training with Better Locality and Parallelism|
|6||Grouped Sparse Projection for Deep Learning|
|7||Gradient descent with momentum using dynamic stochastic computing|
|8||Memory-Bounded Sparse Training on the Edge|
|9||A Fast Method to Fine-tune Neural Networks for the Least Energy Consumption on FPGAs|
|10||Self-reflective Variational Autoencoder|
|11||Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions|
|12||An Exact Penalty Method for Binary Training|
|13||Training CNNs faster with Input and Kernel Downsampling|
|14||On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Method|
|15||MoIL: Enabling Efficient Incremental Training on Edge Devices|
|16||Heterogeneous Zero-Shot Federated Learning with New Classes for Audio Classification|
|17||Scaling Deep Networks with the Mesh Adaptive Direct Search Algorithm|
The table below reports the 3 winners of the competition and their id. Note that those ids are used to identify corresponding posters in the poster session using Gather.town link.
|18||Improving ResNet-9 Generalization Trained on Small Datasets|
|19||Efficient Training Under Limited Resources|
|20||Training a 5000×32×32×3 RGB Dataset on NVIDIA TESLA V100 in 10 Minutes|