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Found 5 papers, 1 papers with code
Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning
Humans learn by interacting with their environments and perceiving the outcomes of their actions.
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception
Harmonized DNNs achieve the best of both worlds and experience attacks that are detectable and affect features that humans find diagnostic for recognition, meaning that attacks on these models are more likely to be rendered ineffective by inducing similar effects on human perception.
Stable and expressive recurrent vision models
We posit that the effectiveness of recurrent vision models is bottlenecked by the standard algorithm used for training them, "back-propagation through time" (BPTT), which has O(N) memory-complexity for training an N step model.
Automatic Inspection of Utility Scale Solar Power Plants using Deep Learning
Solar energy has the potential to become the backbone energy source for the world.
Autoencoders with Variable Sized Latent Vector for Image Compression
Learning to compress images is an interesting and challenging task.
