Advances in Neural Information Processing Systems
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Publication Venue For
- Directed Spectral Measures Improve Latent Network Models Of Neural Populations.. 34:7421-7435. 2021
- A dictionary approach to domain-invariant learning in deep networks. 2020-December. 2020
- A universal approximation theorem of deep neural networks for expressing probability distributions. 2020-December. 2020
- Beyond lazy training for over-parameterized tensor decomposition. 2020-December. 2020
- Explaining landscape connectivity of low-cost solutions for multilayer nets. 32. 2019
- Improving textual network learning with variational homophilic embeddings. 32. 2019
- Kernel-based approaches for sequence modeling: Connections to neural methods. 32. 2019
- Spiderboost and momentum: Faster stochastic variance reduction algorithms. 32. 2019
- Extracting Relationships by Multi-Domain Matching.. 31:6799-6810. 2018
- Learning bounds for greedy approximation with explicit feature maps from multiple kernels. 2018-December:4690-4701. 2018
- Adversarial symmetric variational autoencoder. 2017-December:4331-4340. 2017
- Deconvolutional paragraph representation learning. 2017-December:4170-4180. 2017
- On Optimal Generalizability in Parametric Learning. 30. 2017
- On optimal generalizability in parametric learning. 2017-December:3456-3466. 2017
- Demixing odors - Fast inference in olfaction 2013
- Dynamic clustering via asymptotics of the dependent Dirichlet process mixture 2013
- Efficient supervised sparse analysis and synthesis operators 2013
- Integrated non-factorized variational inference 2013
- Locally adaptive bayesian multivariate time series 2013
- Multiscale dictionary learning for estimating conditional distributions 2013
- Robust multimodal graph matching: Sparse coding meets graph matching 2013
- Augment-and-conquer negative binomial processes. 4:2546-2554. 2012
- Complex inference in neural circuits with probabilistic population codes and topic models. 4:3059-3067. 2012
- Finding exemplars from pairwise dissimilarities via simultaneous sparse recovery. 1:19-27. 2012
- Joint modeling of a matrix with associated text via latent binary features. 2:1556-1564. 2012
- Modelling reciprocating relationships with Hawkes processes. 4:2600-2608. 2012
- Multiresolution Gaussian processes. 1:737-745. 2012
- Provable ICA with unknown Gaussian noise, with implications for Gaussian mixtures and autoencoders. 3:2375-2383. 2012
- Repulsive mixtures. 3:1889-1897. 2012
- Topology constraints in graphical models. 1:791-799. 2012
- Generalized Beta Mixtures of Gaussians. 24:523-531. 2011
- A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation.. 2009:486-494. 2009
- Stratification learning: Detecting mixed density and dimensionality in high dimensional point clouds. 553-560. 2007
- Radial basis function network for multi-task learning. 795-802. 2005
- Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer. 2021
- A Regression Approach to Learning-Augmented Online Algorithms 2021
- CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks 2021
- FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective 2021
- Learning Markov State Abstractions for Deep Reinforcement Learning 2021
- Neural Tangent Kernel Maximum Mean Discrepancy 2021
- On the Representation of Solutions to Elliptic PDEs in Barron Spaces 2021
- Robust Allocations with Diversity Constraints 2021
- Robust Counterfactual Explanations on Graph Neural Networks 2021
- Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks 2021
- Understanding Deflation Process in Over-parametrized Tensor Decomposition 2021
- A finite-time analysis of two time-scale actor-critic methods 2020
- Adaptive probing policies for shortest path routing 2020
- AutoSync: Learning to synchronize for data-parallel distributed deep learning 2020
- Benchmarking deep inverse models over time, and the neural-adjoint method 2020
- Calibrating CNNs for lifelong learning 2020
- GAN memory with no forgetting 2020
- Instance-based generalization in reinforcement learning 2020
- Perturbing across the feature hierarchy to improve standard and strict blackbox attack transferability 2020
- Reconsidering generative objectives for counterfactual reasoning 2020
- Capacity bounded differential privacy 2019
- Certified adversarial robustness with additive noise 2019
- Defending neural backdoors via generative distribution modeling 2019
- Gradient information for representation and modeling 2019
- On fenchel mini-max learning 2019
- Optimal sparse decision trees 2019
- Ouroboros: On accelerating training of transformer-based language models 2019
- Reward constrained interactive recommendation with natural language feedback 2019
- Scalable gromov-wasserstein learning for graph partitioning and matching 2019
- Stochastic gradient hamiltonian monte carlo methods with recursive variance reduction 2019
- The step decay schedule: A near optimal, geometrically decaying learning rate procedure for least squares 2019
- This looks like that: Deep learning for interpretable image recognition 2019
- Adversarial text generation via feature-mover's distance 2018
- Beyond log-concavity: Provable guarantees for sampling multi-modal distributions using simulated tempering langevin Monte Carlo 2018
- Diffusion maps for textual network embedding 2018
- Distilled Wasserstein learning for word embedding and topic modeling 2018
- Generalized inverse optimization through online learning 2018
- Global convergence of Langevin dynamics based algorithms for nonconvex optimization 2018
- Information-based adaptive stimulus selection to optimize communication efficiency in brain-computer interfaces 2018
- On the local minima of the empirical risk 2018
- Stochastic nested variance reduction for nonconvex optimization 2018
- Third-order smoothness helps: Faster stochastic optimization algorithms for finding local minima 2018
- A probabilistic framework for nonlinearities in stochastic neural networks 2017
- A screening rule for ℓ1-regularized ising model estimation 2017
- ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching 2017
- ALICE: Towards understanding adversarial learning for joint distribution matching 2017
- Active exploration for learning symbolic representations 2017
- Adversarial Symmetric Variational Autoencoder 2017
- An Inner-loop Free Solution to Inverse Problems using Deep Neural Networks 2017
- Cross-spectral factor analysis 2017
- On the optimization landscape of tensor decompositions 2017
- Robust and efficient transfer learning with hidden parameter Markov decision processes 2017
- Scalable model selection for belief networks 2017
- Speeding up latent variable Gaussian graphical model estimation via nonconvex optimization 2017
- Targeting EEG/LFP synchrony with neural nets 2017
- TernGrad: Ternary gradients to reduce communication in distributed deep learning 2017
- Triangle generative adversarial networks 2017
- VAE Learning via Stein Variational Gradient Descent 2017
- VAE learning via Stein variational gradient descent 2017
- DECOrrelated feature space partitioning for distributed sparse regression 2016
- Flexible models for microclustering with application to entity resolution 2016
- Learning structured sparsity in deep neural networks 2016
- Linear feature encoding for reinforcement learning 2016
- Matrix completion has no spurious local minimum 2016
- Semiparametric differential graph models 2016
- Stochastic gradient MCMC with stale gradients 2016
- Towards unifying hamiltonian Monte Carlo and Slice sampling 2016
- Variational autoencoder for deep learning of images, labels and captions 2016
- Deep poisson factor modeling 2015
- Discriminative robust transformation learning 2015
- Fast second-order stochastic backpropagation for variational inference 2015
- Large-scale Bayesian multi-label learning via topic-based label embeddings 2015
- On the consistency theory of high dimensional variable screening 2015
- On the convergence of stochastic gradient MCMC algorithms with high-order integrators 2015
- Parallelizing MCMC with random partition trees 2015
- Policy evaluation using the Ω-return 2015
- Probabilistic curve learning: Coulomb repulsion and the electrostatic Gaussian process 2015
- Bayesian nonlinear support vector machines and discriminative factor modeling 2014
- Compressive sensing of signals from a GMM with sparse precision matrices 2014
- Dynamic rank factor model for text streams 2014
- Median selection subset aggregation for parallel inference 2014
- The Bayesian case model: A generative approach for case-based reasoning and prototype classification 2014
- Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models. 2013
- A graphical transformation for belief propagation: Maximum Weight Matchings and odd-sized cycles 2013
- An integer optimization approach to associative classification 2012
- Multiplicative forests for continuous-time processes 2012
- On semi-supervised classification 2005
- On the dynamics of boosting 2004