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Found 10 papers, 1 papers with code
Aurora-M: The First Open Source Multilingual Language Model Red-teamed according to the U.S. Executive Order
Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility.
Learning Causal State Representations of Partially Observable Environments
In this paper, we propose an algorithm to approximate causal states, which are the coarsest partition of the joint history of actions and observations in partially-observable Markov decision processes (POMDP).
Born Again Neural Networks
Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher}) to another (the student).
Question Type Guided Attention in Visual Question Answering
Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions.
Tensor Contraction & Regression Networks
Second, we introduce tensor regression layers, which express the output of a neural network as a low-rank multi-linear mapping from a high-order activation tensor to the softmax layer.
Tensor Regression Networks
First, we introduce Tensor Contraction Layers (TCLs) that reduce the dimensionality of their input while preserving their multilinear structure using tensor contraction.
Compact Tensor Pooling for Visual Question Answering
Performing high level cognitive tasks requires the integration of feature maps with drastically different structure.
Tensor Contraction Layers for Parsimonious Deep Nets
Specifically, we propose the Tensor Contraction Layer (TCL), the first attempt to incorporate tensor contractions as end-to-end trainable neural network layers.
Active Long Term Memory Networks
Continual Learning in artificial neural networks suffers from interference and forgetting when different tasks are learned sequentially.
Sparse Predictive Structure of Deconvolved Functional Brain Networks
The functional and structural representation of the brain as a complex network is marked by the fact that the comparison of noisy and intrinsically correlated high-dimensional structures between experimental conditions or groups shuns typical mass univariate methods.
