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Found 9 papers, 6 papers with code
Towards Understanding How Machines Can Learn Causal Overhypotheses
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence.
Content-Context Factorized Representations for Automated Speech Recognition
Deep neural networks have largely demonstrated their ability to perform automated speech recognition (ASR) by extracting meaningful features from input audio frames.
What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics
While there have been significant gains in the field of automated video description, the generalization performance of automated description models to novel domains remains a major barrier to using these systems in the real world.
Multi-Modal Pre-Training for Automated Speech Recognition
Traditionally, research in automated speech recognition has focused on local-first encoding of audio representations to predict the spoken phonemes in an utterance.
A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery
Urban areas consume over two-thirds of the world's energy and account for more than 70 percent of global CO2 emissions.
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking
Automatic video captioning aims to train models to generate text descriptions for all segments in a video, however, the most effective approaches require large amounts of manual annotation which is slow and expensive.
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn.
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics
Finally we show that diagnostic visualization using LDAM leads to a novel insight into the parameter averaging method for deep net training.
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data
Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples.
