Search Results for author:
Found 30 papers, 14 papers with code
RS-NeRF: Neural Radiance Fields from Rolling Shutter Images
Neural Radiance Fields (NeRFs) have become increasingly popular because of their impressive ability for novel view synthesis.
Torchtree: flexible phylogenetic model development and inference using PyTorch
Furthermore, we explore the use of the forward KL divergence as an optimizing criterion for variational inference, which can handle discontinuous and non-differentiable models.
Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models
This work studies the challenge of aligning large language models (LLMs) with offline preference data.
Motion Blur Decomposition with Cross-shutter Guidance
Experiment results have verified the effectiveness of our proposed algorithm, as well as the validity of our dual imaging setting.
Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks
In fact, human preference data are now used with classic reinforcement learning algorithms such as actor-critic methods, which involve evaluating an intermediate policy over a reward learned from human preference data with distribution shift, known as off-policy evaluation (OPE).
Towards Deep Learning Models Resistant to Transfer-based Adversarial Attacks via Data-centric Robust Learning
Instead, a one-shot adversarial augmentation prior to training is sufficient, and we name this new defense paradigm Data-centric Robust Learning (DRL).
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
As a result, by properly choosing the network size and hyperparameters, NPMD can find an $\epsilon$-optimal policy with $\widetilde{O}(\epsilon^{-\frac{d}{\alpha}-2})$ samples in expectation, where $\alpha\in(0, 1]$ indicates the smoothness of environment.
Hard Adversarial Example Mining for Improving Robust Fairness
Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE).
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems
A popular approach is to utilize human feedback to learn a reward function for training.
Cross-modal Place Recognition in Image Databases using Event-based Sensors
Visual place recognition is an important problem towards global localization in many robotics tasks.
Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time
A crucial problem in reinforcement learning is learning the optimal policy.
Random-effects substitution models for phylogenetics via scalable gradient approximations
Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process.
Single Image Deblurring with Row-dependent Blur Magnitude
Image degradation often occurs during fast camera or object movements, regardless of the exposure modes: global shutter (GS) or rolling shutter (RS).
Rethinking Video Frame Interpolation from Shutter Mode Induced Degradation
Image restoration from various motion-related degradations, like blurry effects recorded by a global shutter (GS) and jello effects caused by a rolling shutter (RS), has been extensively studied.
Blur Interpolation Transformer for Real-World Motion from Blur
This paper studies the challenging problem of recovering motion from blur, also known as joint deblurring and interpolation or blur temporal super-resolution.
TreeFlow: probabilistic programming and automatic differentiation for phylogenetics
Probabilistic programming frameworks are powerful tools for statistical modelling and inference.
Automatic differentiation is no panacea for phylogenetic gradient computation
Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning.
Speeding Up Question Answering Task of Language Models via Inverted Index
Natural language processing applications, such as conversational agents and their question-answering capabilities, are widely used in the real world.
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks
We consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks.
Neural Global Shutter: Learn to Restore Video from a Rolling Shutter Camera with Global Reset Feature
In this paper, we investigate using rolling shutter with a global reset feature (RSGR) to restore clean global shutter (GS) videos.
Optimal Estimation of Off-Policy Policy Gradient via Double Fitted Iteration
Policy gradient (PG) estimation becomes a challenge when we are not allowed to sample with the target policy but only have access to a dataset generated by some unknown behavior policy.
Scalable Bayesian divergence time estimation with ratio transformations
Divergence time estimation is crucial to provide temporal signals for dating biologically important events, from species divergence to viral transmissions in space and time.
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion
We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns.
From viral evolution to spatial contagion: a biologically modulated Hawkes model
Mutations sometimes increase contagiousness for evolving pathogens.
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference
Bootstrapping provides a flexible and effective approach for assessing the quality of batch reinforcement learning, yet its theoretical property is less understood.
PROP: Pre-training with Representative Words Prediction for Ad-hoc Retrieval
Recently pre-trained language representation models such as BERT have shown great success when fine-tuned on downstream tasks including information retrieval (IR).
Urban Bike Lane Planning with Bike Trajectories: Models, Algorithms, and a Real-World Case Study
We formalize the bike lane planning problem in view of the cyclists' utility functions and derive an integer optimization model to maximize the utility.
VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera
Then we use orientation and camera similarity as penalty to get final similarity.
Gradients do grow on trees: a linear-time ${\cal O}\hspace{-0.2em}\left( N \right)$-dimensional gradient for statistical phylogenetics
To make this tractable, we present a linear-time algorithm for ${\cal O}\hspace{-0. 2em}\left( N \right)$-dimensional gradient evaluation and apply it to general continuous-time Markov processes of sequence substitution on a phylogenetic tree without a need to assume either stationarity or reversibility.
Computation Populations and Evolution Methodology
MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching
Text matching is the core problem in many natural language processing (NLP) tasks, such as information retrieval, question answering, and conversation.
