Research Area
Year Published

297 Results

May 25, 2020

Differentiable Gaussian Process Motion Planning

International Conference on Robotics and Automation (ICRA)

We propose a method for leveraging past experience to learn how to automatically adapt the parameters of Gaussian Process Motion Planning (GPMP) algorithms. Specifically, we propose a differentiable extension to the GPMP2 algorithm, so that it can be trained end-to-end from data.

By: Mohak Bhardwaj, Byron Boots, Mustafa Mukadam

May 4, 2020

SeCoST: Sequential Co-Supervision for Weakly Labeled Audio Event Detection

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

Weakly supervised learning algorithms are critical for scaling audio event detection to several hundreds of sound categories. Such learning models should not only disambiguate sound events efficiently with minimal class-specific annotation but also be robust to label noise, which is more apparent with weak labels instead of strong annotations. In this work, we propose a new framework for designing learning models with weak supervision by bridging ideas from sequential learning and knowledge distillation.

By: Anurag Kumar, Vamsi Krishna Ithapu

April 27, 2020

The Early Phase of Neural Network Training

International Conference on Learning Representations (ICLR)

Recent studies have shown that many important aspects of neural network learning take place within the very earliest iterations or epochs of training. For example, sparse, trainable sub-networks emerge (Frankle et al., 2019), gradient descent moves into a small subspace (Gur-Ari et al., 2018), and the network undergoes a critical period (Achille et al., 2019). Here we examine the changes that deep neural networks undergo during this early phase of training.

By: Jonathan Frankle, David J. Schwab, Ari Morcos

April 27, 2020

Generalization through Memorization: Nearest Neighbor Language Models

International Conference on Learning Representations (ICLR)

We introduce kNN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a k-nearest neighbors (kNN) model. The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and can be drawn from any text collection, including the original LM training data.

By: Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis

April 26, 2020

Discovering Motor Programs by Recomposing Demonstrations

International Conference on Learning Representations (ICLR)

In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations.

By: Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto, Abhinav Gupta

April 25, 2020

Permutation Equivariant Models for Compositional Generalization in Language

International Conference on Learning Representations (ICLR)

Humans understand novel sentences by composing meanings and roles of core language components. In contrast, neural network models for natural language modeling fail when such compositional generalization is required. The main contribution of this paper is to hypothesize that language compositionality is a form of group-equivariance. Based on this hypothesis, we propose a set of tools for constructing equivariant sequence-to-sequence models.

By: Jonathan Gordon, David Lopez-Paz, Marco Baroni, Diane Bouchacourt

April 20, 2020

Direction of Arrival Estimation in Highly Reverberant Environments Using Soft Time-Frequency Mask

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

A recent approach to improving the robustness of sound localization in reverberant environments is based on pre-selection of time-frequency pixels that are dominated by direct sound. This approach is equivalent to applying a binary time-frequency mask prior to the localization stage. Although the binary mask approach was shown to be effective, it may not exploit the information available in the captured signal to its full extent. In an attempt to overcome this limitation, it is hereby proposed to employ a soft mask instead of the binary mask.

By: Vladimir Tourbabin, Jacob Donley, Boaz Rafaely, Ravish Mehra

April 9, 2020

Environment-aware reconfigurable noise suppression

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

The paper proposes an efficient, robust, and reconfigurable technique to suppress various types of noises for any sampling rate. The theoretical analyses, subjective and objective test results show that the proposed noise suppression (NS) solution significantly enhances the speech transmission index (STI), speech intelligibility (SI), signal-to-noise ratio (SNR), and subjective listening experience.

By: Jun Yang, Joshua Bingham

March 2, 2020

Federated Optimization in Heterogenous Networks

Conference on Machine Learning and Systems (MLSys)

Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the network (statistical heterogeneity). In this work, we introduce a framework, FedProx, to tackle heterogeneity in federated networks.

By: Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

February 21, 2020

Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection

International Conference on Learning Representations (ICLR)

Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box recommender systems.

By: Michael Tsang, Dehua Cheng, Hanpeng Liu, Xue Feng, Eric Zhou, Yan Liu