Search Results for author:
Found 15 papers, 3 papers with code
Unlocking the Power of Representations in Long-term Novelty-based Exploration
We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space.
BYOL-Explore: Exploration by Bootstrapped Prediction
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments.
Geometric Entropic Exploration
Exploration is essential for solving complex Reinforcement Learning (RL) tasks.
Model-Free Counterfactual Credit Assignment
Credit assignment in reinforcement learning is the problem of measuring an action’s influence on future rewards.
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Credit assignment in reinforcement learning is the problem of measuring an action's influence on future rewards.
Spoken Language Understanding on the Edge
We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications.
Ranked #4 on
Spoken Language Understanding
on Snips-SmartSpeaker
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices.
Ranked #46 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Deep Representation for Patient Visits from Electronic Health Records
We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed.
Spectral Inference Methods on Sparse Graphs: Theory and Applications
In an era of unprecedented deluge of (mostly unstructured) data, graphs are proving more and more useful, across the sciences, as a flexible abstraction to capture complex relationships between complex objects.
Fast Randomized Semi-Supervised Clustering
We consider the problem of clustering partially labeled data from a minimal number of randomly chosen pairwise comparisons between the items.
Clustering from Sparse Pairwise Measurements
We consider the problem of grouping items into clusters based on few random pairwise comparisons between the items.
Random Projections through multiple optical scattering: Approximating kernels at the speed of light
Random projections have proven extremely useful in many signal processing and machine learning applications.
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation
We propose a spectral algorithm for these two tasks called MaCBetH (for Matrix Completion with the Bethe Hessian).
Spectral Detection in the Censored Block Model
We describe two spectral algorithms for this task based on the non-backtracking and the Bethe Hessian operators.
Spectral Clustering of Graphs with the Bethe Hessian
We show that this approach combines the performances of the non-backtracking operator, thus detecting clusters all the way down to the theoretical limit in the stochastic block model, with the computational, theoretical and memory advantages of real symmetric matrices.
