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Found 8 papers, 6 papers with code
Multi-entity Video Transformers for Fine-Grained Video Representation Learning
In this work, we advance the state-of-the-art for this area by re-examining the design of transformer architectures for video representation learning.
Analyzing Populations of Neural Networks via Dynamical Model Embedding
The specific model to be emulated is determined by a model embedding vector that the meta-model takes as input; these model embedding vectors constitute a manifold corresponding to the given population of models.
Spartan: Differentiable Sparsity via Regularized Transportation
We present Spartan, a method for training sparse neural network models with a predetermined level of sparsity.
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training
Self-training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training.
Equivariant Transformer Networks
How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network?
Sketching Linear Classifiers over Data Streams
We introduce a new sub-linear space sketch---the Weight-Median Sketch---for learning compressed linear classifiers over data streams while supporting the efficient recovery of large-magnitude weights in the model.
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
What learning algorithms can be run directly on compressively-sensed data?
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks.
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