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Found 7 papers, 6 papers with code
Speeding up Semantic Segmentation for Autonomous Driving
We propose a novel deep network architecture for image segmentation that keeps the high accuracy while being efficient enough for embedded devices.
RUDDER: Return Decomposition for Delayed Rewards
In MDPs the Q-values are equal to the expected immediate reward plus the expected future rewards.
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Explaining and Interpreting LSTMs
While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved.
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
Modern Hopfield Networks and Attention for Immune Repertoire Classification
We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.
Hopfield Networks is All You Need
The new update rule is equivalent to the attention mechanism used in transformers.
Immune Repertoire Classification
Multiple Instance Learning
+1
Cross-Domain Few-Shot Learning by Representation Fusion
On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.
