Search Results for author:
Found 35 papers, 9 papers with code
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN
Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions.
Ranked #2 on
Multimodal Emotion Recognition
on IEMOCAP
Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds
We propose a language grounding model that attends on the referential utterance and on the object proposal pool computed from a pre-trained detector to decode referenced objects with a detection head, without selecting them from the pool.
Generalization to New Actions in Reinforcement Learning
A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances, such as making decisions from new action choices.
Move to See Better: Self-Improving Embodied Object Detection
Experiments on both indoor and outdoor datasets show that (1) our method obtains high-quality 2D and 3D pseudo-labels from multi-view RGB-D data; (2) fine-tuning with these pseudo-labels improves the 2D detector significantly in the test environment; (3) training a 3D detector with our pseudo-labels outperforms a prior self-supervised method by a large margin; (4) given weak supervision, our method can generate better pseudo-labels for novel objects.
Deep learning via LSTM models for COVID-19 infection forecasting in India
Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences.
Know Your Action Set: Learning Action Relations for Reinforcement Learning
Intelligent agents can solve tasks in a variety of ways depending on the action set at their disposal.
Variance Penalized On-Policy and Off-Policy Actor-Critic
Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent.
NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain
This paper contributes to research in understanding nuclear domain knowledge which is then evaluated on Nuclear Question Answering Dataset (NQuAD) created by nuclear domain experts as part of this research.
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression
Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of Gaussian processes but their training remains challenging.
Uniform Information Density Effects on Syntactic Choice in Hindi
According to the UNIFORM INFORMATION DENSITY (UID) hypothesis (Levy and Jaeger, 2007; Jaeger, 2010), speakers tend to distribute information density across the signal uniformly while producing language.
The Limits of Maxing, Ranking, and Preference Learning
We present a comprehensive understanding of three important problems in PAC preference learning: maximum selection (maxing), ranking, and estimating all pairwise preference probabilities, in the adaptive setting.
A deep learning system for differential diagnosis of skin diseases
In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories).
Optimal Robust Learning of Discrete Distributions from Batches
Previous estimators for this setting ran in exponential time, and for some regimes required a suboptimal number of batches.
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
Sample- and computationally-efficient distribution estimation is a fundamental tenet in statistics and machine learning.
A General Method for Robust Learning from Batches
In many applications, data is collected in batches, some of which are corrupt or even adversarial.
Linear-Sample Learning of Low-Rank Distributions
Many latent-variable applications, including community detection, collaborative filtering, genomic analysis, and NLP, model data as generated by low-rank matrices.
The Price of Tolerance in Distribution Testing
Specifically, we show the sample complexity to be \[\tilde \Theta\left(\frac{\sqrt{n}}{\varepsilon_2^{2}} + \frac{n}{\log n} \cdot \max \left\{\frac{\varepsilon_1}{\varepsilon_2^2},\left(\frac{\varepsilon_1}{\varepsilon_2^2}\right)^{\!\! 2}\right\}\right),\] providing a smooth tradeoff between the two previously known cases.
Language Modulated Detection and Detection Modulated Language Grounding in 2D and 3D Scenes
Object detectors are typically trained on a fixed vocabulary of objects and attributes that is often too restrictive for open-domain language grounding, where the language utterance may refer to visual entities in various levels of abstraction, such as a cat, the leg of a cat, or the stain on the front leg of the chair.
Identifying Collaborative Conversations using Latent Discourse Behaviors
In this work, we study collaborative online conversations.
Does Social Pressure Drive Persuasion in Online Fora?
Online forums such as ChangeMyView have been explored to research aspects of persuasion and argumentative quality in language.
Robust Estimation for Random Graphs
We study the problem of robustly estimating the parameter $p$ of an Erd\H{o}s-R\'enyi random graph on $n$ nodes, where a $\gamma$ fraction of nodes may be adversarially corrupted.
Robust estimation algorithms don't need to know the corruption level
However, their vast majority approach optimal accuracy only when given a tight upper bound on the fraction of corrupt data.
TURF: A Two-factor, Universal, Robust, Fast Distribution Learning Algorithm
We derive a near-linear-time and essentially sample-optimal estimator that establishes $c_{t, d}=2$ for all $(t, d)\ne(1, 0)$.
Deep Reinforcement Agent for Efficient Instant Search
Instant Search is a paradigm where a search system retrieves answers on the fly while typing.
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values.
Efficient List-Decodable Regression using Batches
We begin the study of list-decodable linear regression using batches.
Efficient Multi-Task Reinforcement Learning via Selective Behavior Sharing
The ability to leverage shared behaviors between tasks is critical for sample-efficient multi-task reinforcement learning (MTRL).
Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement
Language is compositional; an instruction can express multiple relation constraints to hold among objects in a scene that a robot is tasked to rearrange.
Supply chain emission estimation using large language models
Large enterprises face a crucial imperative to achieve the Sustainable Development Goals (SDGs), especially goal 13, which focuses on combating climate change and its impacts.
Linear Regression using Heterogeneous Data Batches
A common approach assumes that the sources fall in one of several unknown subgroups, each with an unknown input distribution and input-output relationship.
Generating Drug Repurposing Hypotheses through the Combination of Disease-Specific Hypergraphs
Cosine similarity values are computed between (1) all biological pathways starting at the considered drug and ending at the disease of interest and (2) all biological pathways starting at drugs currently prescribed against that disease and ending at the disease of interest.
ODIN: A Single Model for 2D and 3D Perception
The gap in performance between methods that consume posed images versus post-processed 3D point clouds has fueled the belief that 2D and 3D perception require distinct model architectures.
LatticeGraphNet: A two-scale graph neural operator for simulating lattice structures
This study introduces a two-scale Graph Neural Operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures.
Scaling laws for learning with real and surrogate data
Collecting large quantities of high-quality data is often prohibitively expensive or impractical, and a crucial bottleneck in machine learning.
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function.
