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Found 169 papers, 89 papers with code
Bridging the Gap Between f-GANs and Wasserstein GANs
Generative adversarial networks (GANs) variants approximately minimize divergences between the model and the data distribution using a discriminator.
Reliable Decisions with Threshold Calibration
We propose a stronger notion of calibration called threshold calibration, which is exactly the condition required to ensure that decision loss is predicted accurately for threshold decisions.
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
A structural equation model (SEM) is an effective framework to reason over causal relationships represented via a directed acyclic graph (DAG). Recent advances have enabled effective maximum-likelihood point estimation of DAGs from observational data.
PiRank: Scalable Learning To Rank via Differentiable Sorting
A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods.
HyperSPNs: Compact and Expressive Probabilistic Circuits
Probabilistic circuits (PCs) are a family of generative models which allows for the computation of exact likelihoods and marginals of its probability distributions.
D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation
Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire.
Density Ratio Estimation via Infinitesimal Classification
We then estimate the instantaneous rate of change of the bridge distributions indexed by time (the "time score") -- a quantity defined analogously to data (Stein) scores -- with a novel time score matching objective.
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models
These measurements are typically synthesized from images using a fixed physical model of the measurement process, which hinders the generalization capability of models to unknown measurement processes.
Estimating High Order Gradients of the Data Distribution by Denoising
By leveraging Tweedie's formula on higher order moments, we generalize denoising score matching to estimate higher order derivatives.
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.
Pseudo-Spherical Contrastive Divergence
Energy-based models (EBMs) offer flexible distribution parametrization.
A Unified Framework for Multi-distribution Density Ratio Estimation
Binary density ratio estimation (DRE), the problem of estimating the ratio $p_1/p_2$ given their empirical samples, provides the foundation for many state-of-the-art machine learning algorithms such as contrastive representation learning and covariate shift adaptation.
Equivariant Neural Network for Factor Graphs
Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
We introduce a new image editing and synthesis framework, Stochastic Differential Editing (SDEdit), based on a recent generative model using stochastic differential equations (SDEs).
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
In this work, we introduce a new notion -- \emph{decision calibration} -- that requires the predicted distribution and true distribution to be ``indistinguishable'' to a set of downstream decision-makers.
Multi-Agent Imitation Learning with Copulas
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions, which is essential for understanding physical, social, and team-play systems.
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
In this paper, we propose Conditional Score-based Diffusion models for Imputation (CSDI), a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data.
Featurized Density Ratio Estimation
Density ratio estimation serves as an important technique in the unsupervised machine learning toolbox.
IQ-Learn: Inverse soft-Q Learning for Imitation
In many sequential decision-making problems (e. g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task.
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others.
Temporal Predictive Coding For Model-Based Planning In Latent Space
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire.
Improving Compositionality of Neural Networks by Decoding Representations to Inputs
In traditional software programs, it is easy to trace program logic from variables back to input, apply assertion statements to block erroneous behavior, and compose programs together.
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information
Given such an $\mathcal{A}$, and a prior distribution over $f$, we refer to the problem of inferring the output of $\mathcal{A}$ using $T$ evaluations as Bayesian Algorithm Execution (BAX).
On the Critical Role of Conventions in Adaptive Human-AI Collaboration
Humans can quickly adapt to new partners in collaborative tasks (e. g. playing basketball), because they understand which fundamental skills of the task (e. g. how to dribble, how to shoot) carry over across new partners.
Improved Autoregressive Modeling with Distribution Smoothing
While autoregressive models excel at image compression, their sample quality is often lacking.
Hybrid Mutual Information Lower-bound Estimators for Representation Learning
We illustrate that with one set of representations, the hybrid approach is able to achieve good performance on multiple downstream tasks such as classification, reconstruction, and generation.
Anytime Sampling for Autoregressive Models via Ordered Autoencoding
Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling.
Localized Calibration: Metrics and Recalibration
Probabilistic classifiers output confidence scores along with their predictions, and these confidence scores must be well-calibrated (i. e. reflect the true probability of an event) to be meaningful and useful for downstream tasks.
Neural Network Compression for Noisy Storage Devices
We propose a radically different approach that: (i) employs analog memories to maximize the capacity of each memory cell, and (ii) jointly optimizes model compression and physical storage to maximize memory utility.
Negative Data Augmentation
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
Ranked #6 on
Image Generation
on CIFAR-100
Maximum Likelihood Training of Score-Based Diffusion Models
Score-based diffusion models synthesize samples by reversing a stochastic process that diffuses data to noise, and are trained by minimizing a weighted combination of score matching losses.
Ranked #4 on
Image Generation
on ImageNet 32x32
Understanding Classifiers with Generative Models
Although deep neural networks are effective on supervised learning tasks, they have been shown to be brittle.
H-divergence: A Decision-Theoretic Discrepancy Measure for Two Sample Tests
Based on ideas from decision theory, we investigate a new class of discrepancies that are based on the optimal decision loss.
Non-Markovian Predictive Coding For Planning In Latent Space
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients
We consider the task of training a policy that maximizes reward while minimizing disclosure of certain sensitive state variables through the actions.
PiRank: Learning To Rank via Differentiable Sorting
A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods.
Score-Based Generative Modeling through Stochastic Differential Equations
Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9. 89 and FID of 2. 20, a competitive likelihood of 2. 99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.
Ranked #3 on
Image Generation
on CIFAR-10
Efficient Conditional Pre-training for Transfer Learning
Finally, we improve standard ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.
Geography-Aware Self-Supervised Learning
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks.
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
Decision makers often need to rely on imperfect probabilistic forecasts.
Autoregressive Score Matching
To increase flexibility, we propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariate log-conditionals (scores), which need not be normalized.
Probabilistic Circuits for Variational Inference in Discrete Graphical Models
Inference in discrete graphical models with variational methods is difficult because of the inability to re-parameterize gradients of the Evidence Lower Bound (ELBO).
Imitation with Neural Density Models
We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) using the density as a reward.
Noisy Neural Network Compression for Analog Storage Devices
Efficient compression and storage of neural network (NN) parameters is critical for resource-constrained, downstream machine learning applications.
Denoising Diffusion Implicit Models
Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample.
Understanding Classifier Mistakes with Generative Models
From this observation, we develop a detection criteria for samples on which a classifier is likely to fail at test time.
Privacy Preserving Recalibration under Domain Shift
In an extensive empirical study, we find that our algorithm improves calibration on domain-shift benchmarks under the constraints of differential privacy.
HiPPO: Recurrent Memory with Optimal Polynomial Projections
A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed.
Ranked #6 on
Sequential Image Classification
on Sequential MNIST
Multi-label Contrastive Predictive Coding
We demonstrate that the proposed approach is able to lead to better mutual information estimation, gain empirical improvements in unsupervised representation learning, and beat a current state-of-the-art knowledge distillation method over 10 out of 13 tasks.
Efficient Learning of Generative Models via Finite-Difference Score Matching
Several machine learning applications involve the optimization of higher-order derivatives (e. g., gradients of gradients) during training, which can be expensive in respect to memory and computation even with automatic differentiation.
Belief Propagation Neural Networks
Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems.
Unsupervised Calibration under Covariate Shift
A probabilistic model is said to be calibrated if its predicted probabilities match the corresponding empirical frequencies.
Experience Replay with Likelihood-free Importance Weights
The use of past experiences to accelerate temporal difference (TD) learning of value functions, or experience replay, is a key component in deep reinforcement learning.
A Framework for Sample Efficient Interval Estimation with Control Variates
We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samples.
Individual Calibration with Randomized Forecasting
We show that calibration for individual samples is possible in the regression setup if the predictions are randomized, i. e. outputting randomized credible intervals.
Improved Techniques for Training Score-Based Generative Models
Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization.
Ranked #26 on
Image Generation
on CIFAR-10
Predicting Livelihood Indicators from Community-Generated Street-Level Imagery
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world.
Efficient Poverty Mapping using Deep Reinforcement Learning
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring.
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models.
MOPO: Model-based Offline Policy Optimization
We also characterize the trade-off between the gain and risk of leaving the support of the batch data.
Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks
Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation to collect this valuable data.
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks
Adversarial attacks often involve random perturbations of the inputs drawn from uniform or Gaussian distributions, e. g., to initialize optimization-based white-box attacks or generate update directions in black-box attacks.
Training Deep Energy-Based Models with f-Divergence Minimization
Experimental results demonstrate the superiority of f-EBM over contrastive divergence, as well as the benefits of training EBMs using f-divergences other than KL.
Gaussianization Flows
Iterative Gaussianization is a fixed-point iteration procedure that can transform any continuous random vector into a Gaussian one.
Permutation Invariant Graph Generation via Score-Based Generative Modeling
In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph (a. k. a., the score function).
Predictive Coding for Locally-Linear Control
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.
Learning When and Where to Zoom with Deep Reinforcement Learning
While high resolution images contain semantically more useful information than their lower resolution counterparts, processing them is computationally more expensive, and in some applications, e. g. remote sensing, they can be much more expensive to acquire.
A Theory of Usable Information Under Computational Constraints
We propose a new framework for reasoning about information in complex systems.
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving
Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning.
Generating Interpretable Poverty Maps using Object Detection in Satellite Images
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources.
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds.
Efficient Object Detection in Large Images using Deep Reinforcement Learning
Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images.
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.
Learning representations that accurately capture long-range dependencies in sequential inputs --- including text, audio, and genomic data --- is a key problem in deep learning.
Ranked #2 on
Audio Super-Resolution
on VCTK Multi-Speaker
Approximating Human Judgment of Generated Image Quality
Generative models have made immense progress in recent years, particularly in their ability to generate high quality images.
Approximating the Permanent by Sampling from Adaptive Partitions
Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics.
Fair Generative Modeling via Weak Supervision
Real-world datasets are often biased with respect to key demographic factors such as race and gender.
Bridging the Gap Between $f$-GANs and Wasserstein GANs
Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions.
Weakly Supervised Disentanglement with Guarantees
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning.
Unsupervised Out-of-Distribution Detection with Batch Normalization
Based on this insight, we propose to exploit in-batch dependencies for OoD detection.
Understanding the Limitations of Variational Mutual Information Estimators
Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables.
Domain Adaptive Imitation Learning
We formalize the Domain Adaptive Imitation Learning (DAIL) problem, which is a unified framework for imitation learning in the presence of viewpoint, embodiment, and dynamics mismatch.
Towards Certified Defense for Unrestricted Adversarial Attacks
Our defense draws inspiration from differential privacy, and is based on intentionally adding noise to the classifier's outputs to limit the attacker's knowledge about the parameters.
Cross Domain Imitation Learning
Informally, CDIL is the process of learning how to perform a task optimally, given demonstrations of the task in a distinct domain.
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Critically, our model can infer rewards for new, structurally-similar tasks from a single demonstration.
Ranked #1 on
MuJoCo Games
on Point Maze
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations
Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning.
Ranked #2 on
Audio Super-Resolution
on Voice Bank corpus (VCTK)
Multi-Agent Adversarial Inverse Reinforcement Learning
Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification.
MintNet: Building Invertible Neural Networks with Masked Convolutions
To demonstrate their flexibility, we show that our invertible neural networks are competitive with ResNets on MNIST and CIFAR-10 classification.
Ranked #4 on
Image Generation
on MNIST
Generative Modeling by Estimating Gradients of the Data Distribution
We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching.
Ranked #56 on
Image Generation
on CIFAR-10
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions.
Calibrated Model-Based Deep Reinforcement Learning
Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning.
Learning Neural PDE Solvers with Convergence Guarantees
Partial differential equations (PDEs) are widely used across the physical and computational sciences.
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain.
Sliced Score Matching: A Scalable Approach to Density and Score Estimation
However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions.
Learning to Interpret Satellite Images in Global Scale Using Wikipedia
Despite recent progress in computer vision, finegrained interpretation of satellite images remains challenging because of a lack of labeled training data.
Predicting Economic Development using Geolocated Wikipedia Articles
Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries.
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
Millions of people worldwide are absent from their country's census.
Distributed generation of privacy preserving data with user customization
Distributed devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information specific to the data owner that prevents the release of the data.
Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting
A standard technique to correct this bias is by importance weighting samples from the model by the likelihood ratio under the model and true distributions.
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Sorting input objects is an important step in many machine learning pipelines.
Training Variational Autoencoders with Buffered Stochastic Variational Inference
The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up to large datasets.
Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty
Obtaining reliable data describing local poverty metrics at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world.
Meta-Amortized Variational Inference and Learning
Despite the recent success in probabilistic modeling and their applications, generative models trained using traditional inference techniques struggle to adapt to new distributions, even when the target distribution may be closely related to the ones seen during training.
Reparameterizable Subset Sampling via Continuous Relaxations
Many machine learning tasks require sampling a subset of items from a collection based on a parameterized distribution.
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
By amassing the largest known dataset of bacterial Raman spectra, we are able to apply state-of-the-art deep learning approaches to identify 30 of the most common bacterial pathogens from noisy Raman spectra, achieving antibiotic treatment identification accuracies of 99. 0$\pm$0. 1%.
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
We treat the low-dimensional projections as noisy latent representations of an autoencoder and directly learn both the acquisition (i. e., encoding) and amortized recovery (i. e., decoding) procedures.
Dimensionality Reduction
Unsupervised Representation Learning
Learning Controllable Fair Representations
Learning data representations that are transferable and are fair with respect to certain protected attributes is crucial to reducing unfair decisions while preserving the utility of the data.
Streamlining Variational Inference for Constraint Satisfaction Problems
Several algorithms for solving constraint satisfaction problems are based on survey propagation, a variational inference scheme used to obtain approximate marginal probability estimates for variable assignments.
Neural Joint Source-Channel Coding
For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes.
Bias and Generalization in Deep Generative Models: An Empirical Study
In high dimensional settings, density estimation algorithms rely crucially on their inductive bias.
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference
Stochastic optimization techniques are standard in variational inference algorithms.
NECST: Neural Joint Source-Channel Coding
For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes.
Learning to Interpret Satellite Images Using Wikipedia
Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data.
Multi-Agent Generative Adversarial Imitation Learning
Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal.
Improved Training with Curriculum GANs
We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation.
Modeling Sparse Deviations for Compressed Sensing using Generative Models
In compressed sensing, a small number of linear measurements can be used to reconstruct an unknown signal.
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems.
The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models
A large number of objectives have been proposed to train latent variable generative models.
Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning
Our best models predict infrastructure quality with AUROC scores of 0. 881 on Electricity, 0. 862 on Sewerage, 0. 739 on Piped Water, and 0. 786 on Roads using Landsat 8.
Adversarial Constraint Learning for Structured Prediction
Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws.
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
Large amounts of labeled data are typically required to train deep learning models.
Amortized Inference Regularization
In this paper, we leverage the fact that VAEs rely on amortized inference and propose techniques for amortized inference regularization (AIR) that control the smoothness of the inference model.
Constructing Unrestricted Adversarial Examples with Generative Models
Then, conditioned on a desired class, we search over the AC-GAN latent space to find images that are likely under the generative model and are misclassified by a target classifier.
Tile2Vec: Unsupervised representation learning for spatially distributed data
Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks.
Variational Rejection Sampling
Learning latent variable models with stochastic variational inference is challenging when the approximate posterior is far from the true posterior, due to high variance in the gradient estimates.
End-to-End Learning of Motion Representation for Video Understanding
Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks.
Ranked #36 on
Action Recognition
on UCF101
Best arm identification in multi-armed bandits with delayed feedback
We propose a generalization of the best arm identification problem in stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated with delayed feedback.
Graphite: Iterative Generative Modeling of Graphs
Graphs are a fundamental abstraction for modeling relational data.
Ranked #43 on
Node Classification
on Pubmed
Accelerating Natural Gradient with Higher-Order Invariance
An appealing property of the natural gradient is that it is invariant to arbitrary differentiable reparameterizations of the model.
A DIRT-T Approach to Unsupervised Domain Adaptation
Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.
Approximate Inference via Weighted Rademacher Complexity
Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size.
LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures
In this work, we present LSH Softmax, a method to perform sub-linear learning and inference of the softmax layer in the deep learning setting.
The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling
A variety of learning objectives have been recently proposed for training generative models.
Adversarial Examples for Natural Language Classification Problems
Modern machine learning algorithms are often susceptible to adversarial examples — maliciously crafted inputs that are undetectable by humans but that fool the algorithm into producing undesirable behavior.
Shape optimization in laminar flow with a label-guided variational autoencoder
Computational design optimization in fluid dynamics usually requires to solve non-linear partial differential equations numerically.
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
Our method is applicable to both discrete and continuous action spaces, when competing pathwise methods are limited to the latter.
Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans
Eradicating hunger and malnutrition is a key development goal of the 21st century.
Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce.
Neural Variational Inference and Learning in Undirected Graphical Models
Many problems in machine learning are naturally expressed in the language of undirected graphical models.
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models.
A Survey of Human Activity Recognition Using WiFi CSI
This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers.
Audio Super Resolution using Neural Networks
We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks.
Ranked #3 on
Audio Super-Resolution
on Voice Bank corpus (VCTK)
Learning Hierarchical Features from Deep Generative Models
In this paper, we prove that hierarchical latent variable models do not take advantage of the hierarchical structure when trained with existing variational methods, and provide some limitations on the kind of features existing models can learn.
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search
Inference in log-linear models scales linearly in the size of output space in the worst-case.
A-NICE-MC: Adversarial Training for MCMC
We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties.
InfoVAE: Information Maximizing Variational Autoencoders
A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models.
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum likelihood.
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal.
On the Limits of Learning Representations with Label-Based Supervision
Advances in neural network based classifiers have transformed automatic feature learning from a pipe dream of stronger AI to a routine and expected property of practical systems.
Towards Deeper Understanding of Variational Autoencoding Models
We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound.
Learning Hierarchical Features from Generative Models
In this paper, we prove that hierarchical latent variable models do not take advantage of the hierarchical structure when trained with existing variational methods, and provide some limitations on the kind of features existing models can learn.
Boosted Generative Models
We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes.
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Agricultural monitoring, especially in developing countries, can help prevent famine and support humanitarian efforts.
Variational Bayes on Monte Carlo Steroids
We provide a new approach for learning latent variable models based on optimizing our new bounds on the log-likelihood.
Adaptive Concentration Inequalities for Sequential Decision Problems
A key challenge in sequential decision problems is to determine how many samples are needed for an agent to make reliable decisions with good probabilistic guarantees.
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Arising from many applications at the intersection of decision making and machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify the two main classes of inference, namely maximization (optimization) and marginal inference (counting), and are believed to have higher complexity than both of them.
Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
In many machine learning applications, labeled data is scarce and obtaining more labels is expensive.
Estimating Uncertainty Online Against an Adversary
Assessing uncertainty is an important step towards ensuring the safety and reliability of machine learning systems.
Generative Adversarial Imitation Learning
Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal.
Model-Free Imitation Learning with Policy Optimization
In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations.
Tight Variational Bounds via Random Projections and I-Projections
We show that information projections can be combined with random projections to obtain provable guarantees on the quality of the approximation obtained, regardless of the complexity of the original model.
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction.
Variable Elimination in the Fourier Domain
The ability to represent complex high dimensional probability distributions in a compact form is one of the key insights in the field of graphical models.
Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery
Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining.
Embed and Project: Discrete Sampling with Universal Hashing
We consider the problem of sampling from a probability distribution defined over a high-dimensional discrete set, specified for instance by a graphical model.
Optimization With Parity Constraints: From Binary Codes to Discrete Integration
Many probabilistic inference tasks involve summations over exponentially large sets.
Density Propagation and Improved Bounds on the Partition Function
Given a probabilistic graphical model, its density of states is a function that, for any likelihood value, gives the number of configurations with that probability.
Accelerated Adaptive Markov Chain for Partition Function Computation
We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function.