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09/21/2023
Code Soliloquies for Accurate Calculations in Large Language Models
High-quality conversational datasets are integral to the successful deve...
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05/23/2023
Deduction under Perturbed Evidence: Probing Student Simulation Capabilities of Large Language Models
We explore whether Large Language Models (LLMs) are capable of logical r...
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05/22/2023
Parallel Attention and Feed-Forward Net Design for Pre-training and Inference on Transformers
In this paper, we introduce Parallel Attention and Feed-Forward Net Desi...
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05/22/2023
CLASS Meet SPOCK: An Education Tutoring Chatbot based on Learning Science Principles
We present a design framework called Conversational Learning with Analyt...
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01/05/2023
WIRE: Wavelet Implicit Neural Representations
Implicit neural representations (INRs) have recently advanced numerous v...
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12/19/2022
MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages
This paper investigates the problem of Named Entity Recognition (NER) fo...
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12/13/2022
Foveated Thermal Computational Imaging in the Wild Using All-Silicon Meta-Optics
Foveated imaging provides a better tradeoff between situational awarenes...
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11/20/2022
Overfreezing Meets Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks
We study the generalization behavior of transfer learning of deep neural...
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11/07/2022
Asymptotics of the Sketched Pseudoinverse
We take a random matrix theory approach to random sketching and show an ...
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10/22/2022
A Visual Tour Of Current Challenges In Multimodal Language Models
Transformer models trained on massive text corpora have become the de fa...
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09/29/2022
Batch Normalization Explained
A critically important, ubiquitous, and yet poorly understood ingredient...
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08/01/2022
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization
Transformers have achieved remarkable success in sequence modeling and b...
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05/27/2022
Benign Overparameterization in Membership Inference with Early Stopping
Does a neural network's privacy have to be at odds with its accuracy? In...
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04/07/2022
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors
DeepTensor is a computationally efficient framework for low-rank decompo...
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03/07/2022
Singular Value Perturbation and Deep Network Optimization
We develop new theoretical results on matrix perturbation to shed light ...
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02/23/2022
NeuroView-RNN: It's About Time
Recurrent Neural Networks (RNNs) are important tools for processing sequ...
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02/21/2022
Open-Ended Knowledge Tracing
Knowledge tracing refers to the problem of estimating each student's kno...
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02/07/2022
MINER: Multiscale Implicit Neural Representations
We introduce a new neural signal representation designed for the efficie...
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02/02/2022
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference
A surprising phenomenon in modern machine learning is the ability of a h...
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10/16/2021
Transformer with a Mixture of Gaussian Keys
Multi-head attention is a driving force behind state-of-the-art transfor...
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10/15/2021
NeuroView: Explainable Deep Network Decision Making
Deep neural networks (DNs) provide superhuman performance in numerous co...
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10/11/2021
NFT-K: Non-Fungible Tangent Kernels
Deep neural networks have become essential for numerous applications due...
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10/08/2021
Evaluating generative networks using Gaussian mixtures of image features
We develop a measure for evaluating the performance of generative networ...
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10/06/2021
Unrolling Particles: Unsupervised Learning of Sampling Distributions
Particle filtering is used to compute good nonlinear estimates of comple...
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09/09/2021
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
We study the problem of generating arithmetic math word problems (MWPs) ...
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09/06/2021
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
The rapid recent progress in machine learning (ML) has raised a number o...
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06/14/2021
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization
Among the most successful methods for sparsifying deep (neural) networks...
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04/15/2021
NePTuNe: Neural Powered Tucker Network for Knowledge Graph Completion
Knowledge graphs link entities through relations to provide a structured...
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03/09/2021
Transfer Learning Can Outperform the True Prior in Double Descent Regularization
We study a fundamental transfer learning process from source to target l...
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10/27/2020
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels
High dimensionality poses many challenges to the use of data, from visua...
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07/23/2020
Diagnostic Questions:The NeurIPS 2020 Education Challenge
Digital technologies are becoming increasingly prevalent in education, e...
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06/25/2020
Ensembles of Generative Adversarial Networks for Disconnected Data
Most current computer vision datasets are composed of disconnected sets,...
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06/17/2020
Analytical Probability Distributions and EM-Learning for Deep Generative Networks
Deep Generative Networks (DGNs) with probabilistic modeling of their out...
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06/13/2020
Interpretable Super-Resolution via a Learned Time-Series Representation
We develop an interpretable and learnable Wigner-Ville distribution that...
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06/12/2020
LaRVAE: Label Replacement VAE for Semi-Supervised Disentanglement Learning
Learning interpretable and disentangled representations is a crucial yet...
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06/12/2020
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks
We study the transfer learning process between two linear regression pro...
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06/12/2020
MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Designing deep neural networks is an art that often involves an expensiv...
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06/01/2020
Attention Word Embedding
Word embedding models learn semantically rich vector representations of ...
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05/25/2020
qDKT: Question-centric Deep Knowledge Tracing
Knowledge tracing (KT) models, e.g., the deep knowledge tracing (DKT) mo...
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05/12/2020
Deep Learning Techniques for Inverse Problems in Imaging
Recent work in machine learning shows that deep neural networks can be u...
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02/25/2020
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
We study the linear subspace fitting problem in the overparameterized se...
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02/24/2020
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent
Stochastic gradient descent (SGD) with constant momentum and its variant...
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12/09/2019
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Continuous Normalizing Flows (CNFs) have emerged as promising deep gener...
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10/10/2019
The Implicit Regularization of Ordinary Least Squares Ensembles
Ensemble methods that average over a collection of independent predictor...
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09/26/2019
Drawing early-bird tickets: Towards more efficient training of deep networks
(Frankle & Carbin, 2019) shows that there exist winning tickets (small b...
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07/10/2019
Out-of-Distribution Detection Using Neural Rendering Generative Models
Out-of-distribution (OoD) detection is a natural downstream task for dee...
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05/22/2019
Thresholding Graph Bandits with GrAPL
In this paper, we introduce a new online decision making paradigm that w...
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05/21/2019
IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election
The 2016 United States presidential election has been characterized as a...
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02/27/2019
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
We investigate the internal representations that a recurrent neural netw...
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02/25/2019