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Found 7 papers, 4 papers with code
Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training
Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware.
3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network
This paper presents a sparse Change-Based Convolutional Long Short-Term Memory (CB-ConvLSTM) model for event-based eye tracking, key for next-generation wearable healthcare technology such as AR/VR headsets.
Deep Polarization Reconstruction With PDAVIS Events
The polarization event camera PDAVIS is a novel bio-inspired neuromorphic vision sensor that reports both conventional polarization frames and asynchronous, continuously per-pixel polarization brightness changes (polarization events) with fast temporal resolution and large dynamic range.
Exploiting Spatial Sparsity for Event Cameras with Visual Transformers
The input to the ViT consists of events that are accumulated into time bins and spatially separated into non-overlapping sub-regions called patches.
GRUBERT: A GRU-Based Method to Fuse BERT Hidden Layers for Twitter Sentiment Analysis
In this work, we introduce a GRU-based architecture called GRUBERT that learns to map the different BERT hidden layers to fused embeddings with the aim of achieving high accuracy on the Twitter sentiment analysis task.
Understanding (Non-)Robust Feature Disentanglement and the Relationship Between Low- and High-Dimensional Adversarial Attacks
Recent work has put forth the hypothesis that adversarial vulnerabilities in neural networks are due to them overusing "non-robust features" inherent in the training data.
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness).
