Search Results for author:
Found 80 papers, 46 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.
Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models
We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy.
AGG: Amortized Generative 3D Gaussians for Single Image to 3D
To overcome these challenges, we introduce an Amortized Generative 3D Gaussian framework (AGG) that instantly produces 3D Gaussians from a single image, eliminating the need for per-instance optimization.
DiffiT: Diffusion Vision Transformers for Image Generation
In this paper, we study the effectiveness of ViTs in diffusion-based generative learning and propose a new model denoted as Diffusion Vision Transformers (DiffiT).
Ranked #15 on
Image Generation
on ImageNet 256x256
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
In this work, we present a novel subgame curriculum learning framework for zero-sum games.
SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models
However, diffusion models require careful tuning of inference hyperparameters on a validation set and are still sensitive to distribution shifts during testing.
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
Existing digital sensors capture images at fixed spatial and spectral resolutions (e. g., RGB, multispectral, and hyperspectral images), and each combination requires bespoke machine learning models.
Improved Order Analysis and Design of Exponential Integrator for Diffusion Models Sampling
By reformulating the differential equations in DMs and capitalizing on the theory of exponential integrators, we propose refined EI solvers that fulfill all the order conditions, which we designate as Refined Exponential Solver (RES).
Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions
So when applied to large-scale real-world GPS coordinate datasets, which require distance metric learning on the spherical surface, both types of models can fail due to the map projection distortion problem (2D) and the spherical-to-Euclidean distance approximation error (3D).
A Variational Perspective on Solving Inverse Problems with Diffusion Models
To cope with this challenge, we propose a variational approach that by design seeks to approximate the true posterior distribution.
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations
To directly leverage the abundant geospatial information associated with images in pre-training, fine-tuning, and inference stages, we present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images.
DiffCollage: Parallel Generation of Large Content with Diffusion Models
We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content.
Seer: Language Instructed Video Prediction with Latent Diffusion Models
Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals.
Affordance Diffusion: Synthesizing Hand-Object Interactions
In contrast, in this work we focus on synthesizing complex interactions (ie, an articulated hand) with a given object.
Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching
In this case, a well-known issue is the distribution shift between the learned policy and the behavior policy that collects the offline data.
Score-based Diffusion Models in Function Space
They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.
PhysDiff: Physics-Guided Human Motion Diffusion Model
Specifically, we propose a physics-based motion projection module that uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically-plausible motion.
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages.
Ranked #14 on
Text-to-Image Generation
on MS COCO
Concrete Score Matching: Generalized Score Matching for Discrete Data
To this end, we propose an analogous score function called the "Concrete score", a generalization of the (Stein) score for discrete settings.
JPEG Artifact Correction using Denoising Diffusion Restoration Models
Diffusion models can be used as learned priors for solving various inverse problems.
A General Recipe for Likelihood-free Bayesian Optimization
To extend BO to a broader class of models and utilities, we propose likelihood-free BO (LFBO), an approach based on likelihood-free inference.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster
To address this challenge, we have open sourced BigDL 2. 0 at https://github. com/intel-analytics/BigDL/ under Apache 2. 0 license (combining the original BigDL and Analytics Zoo projects); using BigDL 2. 0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9. 6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases).
Dual Diffusion Implicit Bridges for Image-to-Image Translation
Image translation with DDIBs relies on two diffusion models trained independently on each domain, and is a two-step process: DDIBs first obtain latent encodings for source images with the source diffusion model, and then decode such encodings using the target model to construct target images.
LISA: Learning Interpretable Skill Abstractions from Language
Learning policies that effectively utilize language instructions in complex, multi-task environments is an important problem in sequential decision-making.
Denoising Diffusion Restoration Models
Many interesting tasks in image restoration can be cast as linear inverse problems.
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling
We show empirically that the proposed framework achieves strong performance on estimating the number of buildings in the United States and Africa, cars in Kenya, brick kilns in Bangladesh, and swimming pools in the U. S., while requiring as few as 0. 01% of satellite images compared to an exhaustive approach.
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.
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.
Pseudo-Spherical Contrastive Divergence
Energy-based models (EBMs) offer flexible distribution parametrization.
Comparing Distributions by Measuring Differences that Affect Decision Making
Measuring the discrepancy between two probability distributions is a fundamental problem in machine learning and statistics.
Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces
Location encoding is valuable for a multitude of tasks where both the absolute positions and local contexts (image, text, and other types of metadata) of spatial objects are needed for accurate predictions.
SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
The key challenge is balancing faithfulness to the user input (e. g., hand-drawn colored strokes) and realism of the synthesized image.
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.
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.
Ranked #1 on
MuJoCo Games
on Walker2d
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.
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.
Negative Data Augmentation
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
Ranked #8 on
Image Generation
on CIFAR-100
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.
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.
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.
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.
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.
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.
Belief Propagation Neural Networks
Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems.
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.
Robust and On-the-fly Dataset Denoising for Image Classification
We address this problem by reasoning counterfactually about the loss distribution of examples with uniform random labels had they were trained with the real examples, and use this information to remove noisy examples from the training set.
Ranked #35 on
Image Classification
on mini WebVision 1.0
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).
A Theory of Usable Information Under Computational Constraints
We propose a new framework for reasoning about information in complex systems.
Bridging the Gap Between $f$-GANs and Wasserstein GANs
Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions.
Unsupervised Out-of-Distribution Detection with Batch Normalization
Based on this insight, we propose to exploit in-batch dependencies for OoD detection.
Out-of-Distribution Detection
Out of Distribution (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.
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.
Multi-Agent Adversarial Inverse Reinforcement Learning
Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification.
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.
Deep Reinforcement Learning
Model-based Reinforcement Learning
+2
Better Generalization with On-the-fly Dataset Denoising
Memorization in over-parameterized neural networks can severely hurt generalization in the presence of mislabeled examples.
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.
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.
Bias and Generalization in Deep Generative Models: An Empirical Study
In high dimensional settings, density estimation algorithms rely crucially on their inductive bias.
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.
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.
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.
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.
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients
In this technical report, we consider an approach that combines the PPO objective and K-FAC natural gradient optimization, for which we call PPOKFAC.
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.
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.
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.
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.
Factored Temporal Sigmoid Belief Networks for Sequence Learning
Deep conditional generative models are developed to simultaneously learn the temporal dependencies of multiple sequences.
Max-Margin Nonparametric Latent Feature Models for Link Prediction
Our approach attempts to unite the ideas of max-margin learning and Bayesian nonparametrics to discover discriminative latent features for link prediction.
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation
We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features.
