Search Results for author:
Found 138 papers, 56 papers with code
3D Data Augmentation for Driving Scenes on Camera
The proposed data augmentation approach contributes to a gain of 1. 7% and 1. 4% in terms of detection accuracy, on Waymo and nuScences respectively.
Controllable Mesh Generation Through Sparse Latent Point Diffusion Models
In this work, we design a novel sparse latent point diffusion model for mesh generation.
Prototype-based Embedding Network for Scene Graph Generation
Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs.
Learning Universal Policies via Text-Guided Video Generation
The proposed policy-as-video formulation can further represent environments with different state and action spaces in a unified space of images, which, for example, enables learning and generalization across a variety of robot manipulation tasks.
Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production
Amateurs working on mini-films and short-form videos usually spend lots of time and effort on the multi-round complicated process of setting and adjusting scenes, plots, and cameras to deliver satisfying video shots.
The Role of Baselines in Policy Gradient Optimization
Instead, the analysis reveals that the primary effect of the value baseline is to \textbf{reduce the aggressiveness of the updates} rather than their variance.
Correspondence Distillation from NeRF-based GAN
The neural radiance field (NeRF) has shown promising results in preserving the fine details of objects and scenes.
Latent Variable Representation for Reinforcement Learning
Theoretically, we establish the sample complexity of the proposed approach in the online and offline settings.
Model-based Reinforcement Learning
reinforcement-learning
+1
3DHumanGAN: Towards Photo-Realistic 3D-Aware Human Image Generation
We present 3DHumanGAN, a 3D-aware generative adversarial network (GAN) that synthesizes images of full-body humans with consistent appearances under different view-angles and body-poses.
Self-Supervised Geometry-Aware Encoder for Style-Based 3D GAN Inversion
In this paper, we study the challenging problem of 3D GAN inversion where a latent code is predicted given a single face image to faithfully recover its 3D shapes and detailed textures.
Score-based Continuous-time Discrete Diffusion Models
Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data.
Learning to Optimize with Stochastic Dominance Constraints
In real-world decision-making, uncertainty is important yet difficult to handle.
Oracle Inequalities for Model Selection in Offline Reinforcement Learning
We propose the first model selection algorithm for offline RL that achieves minimax rate-optimal oracle inequalities up to logarithmic factors.
Factor Investing with a Deep Multi-Factor Model
Modeling and characterizing multiple factors is perhaps the most important step in achieving excess returns over market benchmarks.
Rethinking Trajectory Prediction via "Team Game"
To accurately predict trajectories in multi-agent settings, e. g. team games, it is important to effectively model the interactions among agents.
Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows
The ability to choose an appropriate camera view among multiple cameras plays a vital role in TV shows delivery.
Improving GANs with A Dynamic Discriminator
Two capacity adjusting schemes are developed for training GANs under different data regimes: i) given a sufficient amount of training data, the discriminator benefits from a progressively increased learning capacity, and ii) when the training data is limited, gradually decreasing the layer width mitigates the over-fitting issue of the discriminator.
Spectral Decomposition Representation for Reinforcement Learning
Representation learning often plays a critical role in reinforcement learning by managing the curse of dimensionality.
Transformer with Implicit Edges for Particle-based Physics Simulation
Consequently, in this paper we propose a novel Transformer-based method, dubbed as Transformer with Implicit Edges (TIE), to capture the rich semantics of particle interactions in an edge-free manner.
BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis
Estimating human keypoints from these videos is difficult due to the complexity of the dance, as well as the multiple moving cameras recording setup.
Monocular 3D Object Reconstruction with GAN Inversion
Reconstruction is achieved by searching for a latent space in the 3D GAN that best resembles the target mesh in accordance with the single view observation.
Making Linear MDPs Practical via Contrastive Representation Learning
It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations.
Discrete Langevin Sampler via Wasserstein Gradient Flow
It is known that gradient-based MCMC samplers for continuous spaces, such as Langevin Monte Carlo (LMC), can be derived as particle versions of a gradient flow that minimizes KL divergence on a Wasserstein manifold.
Guided Diffusion Model for Adversarial Purification
In this paper, we propose a novel purification approach, referred to as guided diffusion model for purification (GDMP), to help protect classifiers from adversarial attacks.
Towards Diverse and Natural Scene-aware 3D Human Motion Synthesis
The ability to synthesize long-term human motion sequences in real-world scenes can facilitate numerous applications.
Accelerating Diffusion Models via Early Stop of the Diffusion Process
By modeling the reverse process of gradually diffusing the data distribution into a Gaussian distribution, generating a sample in DDPMs can be regarded as iteratively denoising a randomly sampled Gaussian noise.
TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing
In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing.
Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation
To enhance the quality of synthesized gestures, we develop a contrastive learning strategy based on audio-text alignment for better audio representations.
Ranked #2 on
Gesture Generation
on TED Gesture Dataset
DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation
This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch.
Ranked #1 on
2D Human Pose Estimation
on JHMDB (2D poses only)
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition
However, we identify these techniques are not well equipped for safe policy learning because they ignore negative experiences(e. g., unsafe or unsuccessful), focusing only on positive experiences, which harms their ability to generalize to new tasks safely.
Model Selection in Batch Policy Optimization
We formalize the problem in the contextual bandit setting with linear model classes by identifying three sources of error that any model selection algorithm should optimally trade-off in order to be competitive: (1) approximation error, (2) statistical complexity, and (3) coverage.
Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition
Typically in recent work, the pseudo-labels are obtained by training a model on the labeled data, and then using confident predictions from the model to teach itself.
BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering
The wide span of viewing positions within these scenes yields multi-scale renderings with very different levels of detail, which poses great challenges to neural radiance field and biases it towards compromised results.
Extract Free Dense Labels from CLIP
Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition.
Towards understanding retrosynthesis by energy-based models
In this paper, we propose a framework that unifies sequence- and graph-based methods as energy-based models (EBMs) with different energy functions.
Neural Stochastic Dual Dynamic Programming
Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-stage stochastic optimization, widely used for modeling real-world process optimization tasks.
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning
Representation learning lies at the heart of the empirical success of deep learning for dealing with the curse of dimensionality.
Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data
Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images.
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis
In this paper, we propose Generative Occupancy Fields (GOF), a novel model based on generative radiance fields that can learn compact object surfaces without impeding its training convergence.
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
Motivated by the observation that a 3D object should look realistic from multiple viewpoints, these methods introduce a multi-view constraint as regularization to learn valid 3D radiance fields from 2D images.
Understanding the Effect of Stochasticity in Policy Optimization
We study the effect of stochasticity in on-policy policy optimization, and make the following four contributions.
SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
There are two important reasoning tasks on KGs: (1) single-hop knowledge graph completion, which involves predicting individual links in the KG; and (2), multi-hop reasoning, where the goal is to predict which KG entities satisfy a given logical query.
MeshInversion: 3D textured mesh reconstruction with generative prior
Reconstruction is achieved by searching for a latent space in the 3D GAN that best resembles the target mesh in accordance with the single view observation.
Understanding and Leveraging Overparameterization in Recursive Value Estimation
To better understand the utility of deep models in RL we present an analysis of recursive value estimation using overparameterized linear representations that provides useful, transferable findings.
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition
Though many reinforcement learning (RL) problems involve learning policies in settings that are difficult to specify safety constraints and sparse rewards, current methods struggle to rapidly and safely acquire successful policies.
SiT: Simulation Transformer for Particle-based Physics Simulation
However, they force particles to interact with all neighbors without selection, and they fall short in capturing material semantics for different particles, leading to unsatisfactory performance, especially in generalization.
Combiner: Full Attention Transformer with Sparse Computation Cost
However, the key limitation of transformers is their quadratic memory and time complexity $\mathcal{O}(L^2)$ with respect to the sequence length in attention layers, which restricts application in extremely long sequences.
Ranked #2 on
Language Modelling
on Wiki-40B
Safe Exploration by Solving Early Terminated MDP
Safe exploration is crucial for the real-world application of reinforcement learning (RL).
The Curse of Passive Data Collection in Batch Reinforcement Learning
In high stake applications, active experimentation may be considered too risky and thus data are often collected passively.
Optimization Variance: Exploring Generalization Properties of DNNs
Unlike the conventional wisdom in statistical learning theory, the test error of a deep neural network (DNN) often demonstrates double descent: as the model complexity increases, it first follows a classical U-shaped curve and then shows a second descent.
Scene-aware Generative Network for Human Motion Synthesis
We revisit human motion synthesis, a task useful in various real world applications, in this paper.
Leveraging Non-uniformity in First-order Non-convex Optimization
Classical global convergence results for first-order methods rely on uniform smoothness and the \L{}ojasiewicz inequality.
Revisiting Skeleton-based Action Recognition
In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.
Ranked #1 on
Skeleton Based Action Recognition
on NTU RGB+D
Unsupervised 3D Shape Completion through GAN Inversion
In contrast to previous fully supervised approaches, in this paper we present ShapeInversion, which introduces Generative Adversarial Network (GAN) inversion to shape completion for the first time.
Visually Informed Binaural Audio Generation without Binaural Audios
Moreover, combined with binaural recordings, our method is able to further boost the performance of binaural audio generation under supervised settings.
On the Optimality of Batch Policy Optimization Algorithms
First, we introduce a class of confidence-adjusted index algorithms that unifies optimistic and pessimistic principles in a common framework, which enables a general analysis.
Nearly Horizon-Free Offline Reinforcement Learning
To the best of our knowledge, these are the \emph{first} set of nearly horizon-free bounds for episodic time-homogeneous offline tabular MDP and linear MDP with anchor points.
DeepStyle: User Style Embedding for Authorship Attribution of Short Texts
Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications.
Off-Policy Imitation Learning from Observations
To further accelerate the learning procedure, we regulate the policy update with an inverse action model, which assists distribution matching from the perspective of mode-covering.
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments.
BlockPlanner: City Block Generation With Vectorized Graph Representation
City modeling is the foundation for computational urban planning, navigation, and entertainment.
Self-Supervised Continuous Control without Policy Gradient
Despite the remarkable progress made by the policy gradient algorithms in reinforcement learning (RL), sub-optimal policies usually result from the local exploration property of the policy gradient update.
Slow Control System for PandaX-III experiment
The PandaX-III experiment uses high pressure gaseous time projection chamber to search for the neutrinoless double beta decay of $^{136}$Xe.
Anomaly Detection
High Energy Physics - Experiment
Instrumentation and Detectors
Focal Frequency Loss for Image Reconstruction and Synthesis
In this study, we show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further.
Offline Policy Selection under Uncertainty
More importantly, we show how the belief distribution estimated by BayesDICE may be used to rank policies with respect to any arbitrary downstream policy selection metric, and we empirically demonstrate that this selection procedure significantly outperforms existing approaches, such as ranking policies according to mean or high-confidence lower bound value estimates.
Escaping the Gravitational Pull of Softmax
Both findings are based on an analysis of convergence rates using the Non-uniform \L{}ojasiewicz (N\L{}) inequalities.
Differentiable Top-k with Optimal Transport
Finding the k largest or smallest elements from a collection of scores, i. e., top-k operation, is an important model component widely used in information retrieval, machine learning, and data mining.
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach
We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation.
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
In this paper we propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data, where parameter gradients are estimated using a learned sampler that mimics local search.
Do 2D GANs Know 3D Shape? Unsupervised 3D shape reconstruction from 2D Image GANs
Through our investigation, we found that such a pre-trained GAN indeed contains rich 3D knowledge and thus can be used to recover 3D shape from a single 2D image in an unsupervised manner.
Named Entity Recognition for Social Media Texts with Semantic Augmentation
In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively.
Ranked #4 on
Chinese Named Entity Recognition
on Weibo NER
Chinese Named Entity Recognition
named-entity-recognition
+3
CoinDICE: Off-Policy Confidence Interval Estimation
We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning, where the goal is to estimate a confidence interval on a target policy's value, given only access to a static experience dataset collected by unknown behavior policies.
Differentiable Top-$k$ with Optimal Transport
The top-$k$ operation, i. e., finding the $k$ largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.
Small Towers Make Big Differences
A delicate balance between multi-task generalization and multi-objective optimization is therefore needed for finding a better trade-off between efficiency and generalization.
Energy-based View of Retrosynthesis
Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery.
Ranked #1 on
Single-step retrosynthesis
on USPTO-50k
Off-Policy Evaluation via the Regularized Lagrangian
The recently proposed distribution correction estimation (DICE) family of estimators has advanced the state of the art in off-policy evaluation from behavior-agnostic data.
Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach
We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation.
Unsupervised Landmark Learning from Unpaired Data
Recent attempts for unsupervised landmark learning leverage synthesized image pairs that are similar in appearance but different in poses.
Scalable Deep Generative Modeling for Sparse Graphs
Based on this, we develop a novel autoregressive model, named BiGG, that utilizes this sparsity to avoid generating the full adjacency matrix, and importantly reduces the graph generation time complexity to $O((n + m)\log n)$.
Video Representation Learning with Visual Tempo Consistency
Visual tempo, which describes how fast an action goes, has shown its potential in supervised action recognition.
Zeroth-Order Supervised Policy Improvement
However, PG algorithms rely on exploiting the value function being learned with the first-order update locally, which results in limited sample efficiency.
Novel Policy Seeking with Constrained Optimization
In problem-solving, we humans can come up with multiple novel solutions to the same problem.
Intra- and Inter-Action Understanding via Temporal Action Parsing
Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features.
Evolutionary Stochastic Policy Distillation
Solving the Goal-Conditioned Reward Sparse (GCRS) task is a challenging reinforcement learning problem due to the sparsity of reward signals.
FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding
To take action recognition to a new level, we develop FineGym, a new dataset built on top of gymnastic videos.
Temporal Pyramid Network for Action Recognition
Previous works often capture the visual tempo through sampling raw videos at multiple rates and constructing an input-level frame pyramid, which usually requires a costly multi-branch network to handle.
Ranked #87 on
Action Recognition
on Something-Something V2
Self-Supervised Scene De-occlusion
This is achieved via Partial Completion Network (PCNet)-mask (M) and -content (C), that learn to recover fractions of object masks and contents, respectively, in a self-supervised manner.
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
SAIL bridges the advantages of IL and RL to reduce the sample complexity substantially, by effectively exploiting sup-optimal demonstrations and efficiently exploring the environment to surpass the demonstrated performance.
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
Learning a good image prior is a long-term goal for image restoration and manipulation.
Energy-Based Processes for Exchangeable Data
Recently there has been growing interest in modeling sets with exchangeability such as point clouds.
Batch Stationary Distribution Estimation
We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions.
GenDICE: Generalized Offline Estimation of Stationary Values
An important problem that arises in reinforcement learning and Monte Carlo methods is estimating quantities defined by the stationary distribution of a Markov chain.
Differentiable Top-k Operator with Optimal Transport
The top-k operation, i. e., finding the k largest or smallest elements from a collection of scores, is an important model component, which is widely used in information retrieval, machine learning, and data mining.
Real or Not Real, that is the Question
While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a random variable that can be estimated from multiple angles.
Reinforcement Learning via Fenchel-Rockafellar Duality
We review basic concepts of convex duality, focusing on the very general and supremely useful Fenchel-Rockafellar duality.
Retrosynthesis Prediction with Conditional Graph Logic Network
Retrosynthesis is one of the fundamental problems in organic chemistry.
Ranked #8 on
Single-step retrosynthesis
on USPTO-50k
AlgaeDICE: Policy Gradient from Arbitrary Experience
In many real-world applications of reinforcement learning (RL), interactions with the environment are limited due to cost or feasibility.
Overcoming Catastrophic Forgetting by Generative Regularization
In this paper, we propose a new method to overcome catastrophic forgetting by adding generative regularization to Bayesian inference framework.
Energy-Inspired Models: Learning with Sampler-Induced Distributions
Motivated by this, we consider the sampler-induced distribution as the model of interest and maximize the likelihood of this model.
Learning with Social Influence through Interior Policy Differentiation
In this work we model the social influence into the scheme of reinforcement learning, enabling the agents to learn both from the environment and from their peers.
Recursive Visual Sound Separation Using Minus-Plus Net
Sounds provide rich semantics, complementary to visual data, for many tasks.
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
In contrast to previous approaches, our algorithm is agnostic to knowledge of the behavior policy (or policies) used to generate the dataset.
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure.
Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural networks.
Feature Intertwiner for Object Detection
We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher behavior and thus pushing towards a more compact class centroid in the feature space.
Ranked #147 on
Object Detection
on COCO test-dev
Revisiting Auxiliary Latent Variables in Generative Models
The success of enriching the variational family with auxiliary latent variables motivates applying the same techniques to the generative model.
Learning to Defense by Learning to Attack
From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces.
Meta Architecture Search
Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks.
Coupled Variational Bayes via Optimization Embedding
This flexible function class couples the variational distribution with the original parameters in the graphical models, allowing end-to-end learning of the graphical models by back-propagation through the variational distribution.
Predictive Approximate Bayesian Computation via Saddle Points
Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable.
Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum log-likelihood estimation for exponential family distributions whose natural parameter resides in a reproducing kernel Hilbert space.
Learning to Defend by Learning to Attack
Adversarial training provides a principled approach for training robust neural networks.
A Neural Compositional Paradigm for Image Captioning
Mainstream captioning models often follow a sequential structure to generate captions, leading to issues such as introduction of irrelevant semantics, lack of diversity in the generated captions, and inadequate generalization performance.
Neural Network Encapsulation
Motivated by the routing to make higher capsule have agreement with lower capsule, we extend the mechanism as a compensation for the rapid loss of information in nearby layers.
Move Forward and Tell: A Progressive Generator of Video Descriptions
We present an efficient framework that can generate a coherent paragraph to describe a given video.
Rethinking the Form of Latent States in Image Captioning
Taking advantage of this, we visually reveal the internal dynamics in the process of caption generation, as well as the connections between input visual domain and output linguistic domain.
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour.
Learning Steady-States of Iterative Algorithms over Graphs
Many graph analytics problems can be solved via iterative algorithms where the solutions are often characterized by a set of steady-state conditions.
Learning towards Minimum Hyperspherical Energy
In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.
Decoupled Networks
Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations.
Syntax-Directed Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous data.
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades.
Boosting the Actor with Dual Critic
This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC.
Deep Hyperspherical Learning
In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres.
Towards Black-box Iterative Machine Teaching
We propose an active teacher model that can actively query the learner (i. e., make the learner take exams) for estimating the learner's status and provably guide the learner to achieve faster convergence.
Contrastive Learning for Image Captioning
Specifically, via two constraints formulated on top of a reference model, the proposed method can encourage distinctiveness, while maintaining the overall quality of the generated captions.
Iterative Machine Teaching
Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner.
Detecting Visual Relationships with Deep Relational Networks
Relationships among objects play a crucial role in image understanding.
Towards Diverse and Natural Image Descriptions via a Conditional GAN
Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect. Sentences produced by existing methods, e. g. those based on RNNs, are often overly rigid and lacking in variability.
Stochastic Generative Hashing
Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases.
Learning from Conditional Distributions via Dual Embeddings
In such problems, each sample $x$ itself is associated with a conditional distribution $p(z|x)$ represented by samples $\{z_i\}_{i=1}^M$, and the goal is to learn a function $f$ that links these conditional distributions to target values $y$.
Discriminative Embeddings of Latent Variable Models for Structured Data
Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design.
Provable Bayesian Inference via Particle Mirror Descent
Bayesian methods are appealing in their flexibility in modeling complex data and ability in capturing uncertainty in parameters.
Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems.
Transductive Learning with Multi-class Volume Approximation
Given a hypothesis space, the large volume principle by Vladimir Vapnik prioritizes equivalence classes according to their volume in the hypothesis space.
Robust Low Rank Kernel Embeddings of Multivariate Distributions
Kernel embedding of distributions has led to many recent advances in machine learning.
Nonparametric Estimation of Multi-View Latent Variable Models
We establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters.
