Search Results for author:
Found 7 papers, 3 papers with code
Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics
With the recent expanding attention of machine learning researchers and practitioners to fairness, there is a void of a common framework to analyze and compare the capabilities of proposed models in deep representation learning.
Successor Feature Sets: Generalizing Successor Representations Across Policies
They also form a natural bridge between model-based and model-free RL methods: like the former they make predictions about future experiences, and like the latter they allow efficient prediction of total discounted rewards.
Increasing Robustness to Spurious Correlations using Forgettable Examples
Neural NLP models tend to rely on spurious correlations between labels and input features to perform their tasks.
Natural Language Inference
Natural Language Understanding
+2
Rapid Adaptation with Conditionally Shifted Neurons
We describe a mechanism by which artificial neural networks can learn rapid adaptation - the ability to adapt on the fly, with little data, to new tasks - that we call conditionally shifted neurons.
Deep Complex Networks
Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models.
Ranked #3 on
Music Transcription
on MusicNet
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.
On Binary Classification with Single-Layer Convolutional Neural Networks
Convolutional neural networks are becoming standard tools for solving object recognition and visual tasks.
