Search Results for author:
Found 59 papers, 18 papers with code
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
For 3D object detection, we instantiate this method as FocalFormer3D, a simple yet effective detector that excels at excavating difficult objects and improving prediction recall.
Ranked #8 on
3D Object Detection
on nuScenes
Differentially Private Video Activity Recognition
In recent years, differential privacy has seen significant advancements in image classification; however, its application to video activity recognition remains under-explored.
PeRFception: Perception using Radiance Fields
The recent progress in implicit 3D representation, i. e., Neural Radiance Fields (NeRFs), has made accurate and photorealistic 3D reconstruction possible in a differentiable manner.
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe.
Neural Scene Representation for Locomotion on Structured Terrain
We propose a learning-based method to reconstruct the local terrain for locomotion with a mobile robot traversing urban environments.
Quantification of Robotic Surgeries with Vision-Based Deep Learning
We believe this is a prerequisite for the provision of surgical feedback and modulation of surgeon performance in pursuit of improved patient outcomes.
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factored action spaces using tensor decompositions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Self-Calibrating Neural Radiance Fields
We also propose a new geometric loss function, viz., projected ray distance loss, to incorporate geometric consistency for complex non-linear camera models.
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Algorithms derived from Tesseract decompose the Q-tensor across agents and utilise low-rank tensor approximations to model agent interactions relevant to the task.
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition
Specifically, we 1) adopt the attention mechanism for both the coach and the players; 2) propose a variational objective to regularize learning; and 3) design an adaptive communication method to let the coach decide when to communicate with the players.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects
Through experimentation and comparative study, we demonstrate the effectiveness of our approach in discovering robust and cost-efficient hand morphologies for grasping novel objects.
Fast Uncertainty Quantification for Deep Object Pose Estimation
Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transfer.
Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces
We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis.
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
Inspired by the original one hundred BPs, we propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning.
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago).
Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery
For both gesture identification and classification datasets, we observed no effect of recurrent classification model choice (LSTM vs. convLSTM) on performance.
Active Learning under Label Shift
We address the problem of active learning under label shift: when the class proportions of source and target domains differ.
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features
We introduce a machine learning method in which energy solutions from the Schrodinger equation are predicted using symmetry adapted atomic orbitals features and a graph neural-network architecture.
Causal Discovery in Physical Systems from Videos
We assume access to different configurations and environmental conditions, i. e., data from unknown interventions on the underlying system; thus, we can hope to discover the correct underlying causal graph without explicit interventions.
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
Learning from spatio-temporal data has numerous applications such as human-behavior analysis, object tracking, video compression, and physics simulation. However, existing methods still perform poorly on challenging video tasks such as long-term forecasting.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data
In this work, we propose a new learning approach for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV).
Compositional Generalization with Tree Stack Memory Units
We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain.
Convolutional Tensor-Train LSTM for Long-Term Video Prediction
Long-term video prediction is highly challenging since it entails simultaneously capturing spatial and temporal information across a long range of image frames. Standard recurrent models are ineffective since they are prone to error propagation and cannot effectively capture higher-order correlations.
Regularized Learning for Domain Adaptation under Label Shifts
We derive a generalization bound for the classifier on the target domain which is independent of the (ambient) data dimensions, and instead only depends on the complexity of the function class.
Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations
Count sketch is a simple popular sketch which uses a randomized hash function to achieve compression.
Neural Lander: Stable Drone Landing Control using Learned Dynamics
To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets.
Policy Gradient in Partially Observable Environments: Approximation and Convergence
Deploying these tools, we generalize a variety of existing theoretical guarantees, such as policy gradient and convergence theorems, to partially observable domains, those which also could be carried to more settings of interest.
Surprising Negative Results for Generative Adversarial Tree Search
We deploy this model and propose generative adversarial tree search (GATS) a deep RL algorithm that learns the environment model and implements Monte Carlo tree search (MCTS) on the learned model for planning.
Question Type Guided Attention in Visual Question Answering
Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions.
Efficient Exploration through Bayesian Deep Q-Networks
This allows us to directly incorporate the uncertainty over the Q-function and deploy Thompson sampling on the learned posterior distribution resulting in efficient exploration/exploitation trade-off.
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs
This is because they mostly rely either on black-box function evaluations that do not capture the structure of the program, or on detailed execution traces that are expensive to obtain, and hence the training data has poor coverage of the domain under consideration.
Deep Active Learning for Named Entity Recognition
In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning.
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods.
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods
We derive finite-time regret bounds for our algorithm with a weak dependence on the dimensionality of the observed space.
Unsupervised learning of transcriptional regulatory networks via latent tree graphical models
We use a latent tree graphical model to analyze gene expression without relying on transcription factor expression as a proxy for regulator activity.
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies
Generally in RL, one can assume a generative model, e. g. graphical models, for the environment, and then the task for the RL agent is to learn the model parameters and find the optimal strategy based on these learnt parameters.
Online and Differentially-Private Tensor Decomposition
In this paper, we resolve many of the key algorithmic questions regarding robustness, memory efficiency, and differential privacy of tensor decomposition.
Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition
More importantly, it is challenging for pre-trained models to obtain word-sequence embeddings that are universally good for all downstream tasks or for any new datasets.
Spectral Methods for Correlated Topic Models
NID distributions are generated through the process of normalizing a family of independent Infinitely Divisible (ID) random variables.
Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods.
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles.
Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Robust tensor CP decomposition involves decomposing a tensor into low rank and sparse components.
Fast and Guaranteed Tensor Decomposition via Sketching
Such tensor contractions are encountered in decomposition methods such as tensor power iterations and alternating least squares.
Convolutional Dictionary Learning through Tensor Factorization
Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning.
Non-convex Robust PCA
In contrast, existing methods for robust PCA, which are based on convex optimization, have $O(m^2n)$ complexity per iteration, and take $O(1/\epsilon)$ iterations, i. e., exponentially more iterations for the same accuracy.
Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor Methods
In the unsupervised setting, we use a simple initialization algorithm based on SVD of the tensor slices, and provide guarantees under the stricter condition that $k\le \beta d$ (where constant $\beta$ can be larger than $1$), where the tensor method recovers the components under a polynomial running time (and exponential in $\beta$).
Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-$1$ Updates
In this paper, we provide local and global convergence guarantees for recovering CP (Candecomp/Parafac) tensor decomposition.
Nonparametric Estimation of Multi-View Latent Variable Models
We establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters.
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
Alternating minimization is a popular heuristic for sparse coding, where the dictionary and the coefficients are estimated in alternate steps, keeping the other fixed.
A Clustering Approach to Learn Sparsely-Used Overcomplete Dictionaries
We consider the problem of learning overcomplete dictionaries in the context of sparse coding, where each sample selects a sparse subset of dictionary elements.
Online Tensor Methods for Learning Latent Variable Models
We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles.
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
This set of higher-order expansion conditions allow for overcomplete models, and require the existence of a perfect matching from latent topics to higher order observed words.
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models
Fitting high-dimensional data involves a delicate tradeoff between faithful representation and the use of sparse models.
Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
The sufficient conditions for identifiability of these models are primarily based on weak expansion constraints on the topic-word matrix, for topic models, and on the directed acyclic graph, for Bayesian networks.
Learning loopy graphical models with latent variables: Efficient methods and guarantees
The problem of structure estimation in graphical models with latent variables is considered.
A Method of Moments for Mixture Models and Hidden Markov Models
Mixture models are a fundamental tool in applied statistics and machine learning for treating data taken from multiple subpopulations.
Spectral Methods for Learning Multivariate Latent Tree Structure
The setting is one where we only have samples from certain observed variables in the tree, and our goal is to estimate the tree structure (i. e., the graph of how the underlying hidden variables are connected to each other and to the observed variables).
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions
We consider the problem of Ising and Gaussian graphical model selection given n i. i. d.
