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Found 10 papers, 3 papers with code
FLex&Chill: Improving Local Federated Learning Training with Logit Chilling
Federated learning are inherently hampered by data heterogeneity: non-iid distributed training data over local clients.
On the Role of ViT and CNN in Semantic Communications: Analysis and Prototype Validation
Semantic communications have shown promising advancements by optimizing source and channel coding jointly.
Curved Representation Space of Vision Transformers
In this paper, we focus on the phenomenon that Transformers show higher robustness against corruptions than CNNs, while not being overconfident.
Demo: Real-Time Semantic Communications with a Vision Transformer
Semantic communications are expected to enable the more effective delivery of meaning rather than a precise transfer of symbols.
How Do Vision Transformers Work?
In particular, we demonstrate the following properties of MSAs and Vision Transformers (ViTs): (1) MSAs improve not only accuracy but also generalization by flattening the loss landscapes.
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
Neural network ensembles, such as Bayesian neural networks (BNNs), have shown success in the areas of uncertainty estimation and robustness.
Differentiable Bayesian Neural Network Inference for Data Streams
Instead of generating separate prediction for each data sample independently, this model estimates the increments of prediction for a new data sample from the previous predictions.
Vector Quantized Bayesian Neural Network Inference for Data Streams
The computational cost of this model is almost the same as that of non-Bayesian NNs.
Attention-based Dialog Embedding for Dialog Breakdown Detection
Dialog Breakdown Detection Challenge 3 of Dialog System Technology Challenge 6
