Search Results for author:
Found 52 papers, 20 papers with code
Self-supervised Motion Representation via Scattering Local Motion Cues
Motion representation is key to many computer vision problems but has never been well studied in the literature.
PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English
To this end, we introduce the Privacy Policy Language Understanding Evaluation (PLUE) benchmark, a multi-task benchmark for evaluating the privacy policy language understanding across various tasks.
FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification
Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data.
Weakly Supervised Online Action Detection for Infant General Movements
Although general movements assessment(GMA) has shown promising results in early CP detection, it is laborious.
Conditional Supervised Contrastive Learning for Fair Text Classification
Contrastive representation learning has gained much attention due to its superior performance in learning representations from both image and sequential data.
CNN-Augmented Visual-Inertial SLAM with Planar Constraints
The CNN depth effectively bootstraps the back-end optimization of SLAM and meanwhile the CNN uncertainty adaptively weighs the contribution of each feature point to the back-end optimization.
FisheyeDistill: Self-Supervised Monocular Depth Estimation with Ordinal Distillation for Fisheye Cameras
In this paper, we deal with the problem of monocular depth estimation for fisheye cameras in a self-supervised manner.
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies
Prior studies in privacy policies frame the question answering (QA) tasks as identifying the most relevant text segment or a list of sentences from the policy document for a user query.
Multi-agent Actor-Critic with Time Dynamical Opponent Model
In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel \textit{Time Dynamical Opponent Model} (TDOM) to encode the knowledge that the opponent policies tend to improve over time.
A Coding Framework and Benchmark towards Compressed Video Understanding
However, in real-world scenarios, the videos are first compressed before the transportation and then decompressed for understanding.
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning
Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems.
Towards Return Parity in Markov Decision Processes
We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies.
Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning
Since GDPR came into force in May 2018, companies have worked on their data practices to comply with this privacy law.
AnANet: Modeling Association and Alignment for Cross-modal Correlation Classification
Thus researchers study the definition of cross-modal correlation category and construct various classification systems and predictive models.
Self-Conditioned Probabilistic Learning of Video Rescaling
After optimization, the downscaled video by our framework preserves more meaningful information, which is beneficial for both the upscaling step and the downstream tasks, e. g., video action recognition task.
EAN: Event Adaptive Network for Enhanced Action Recognition
In this paper, we propose a unified action recognition framework to investigate the dynamic nature of video content by introducing the following designs.
Ranked #8 on
Action Recognition
on Something-Something V1
What and How long: Prediction of Mobile App Engagement
Based on the analysis, we further investigate a novel mobile app engagement prediction problem -- can we predict simultaneously what app the user will use next and how long he/she will stay on that app?
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU
We then identify a sequence of "GPU-friendly" cryptographic protocols to enable privacy-preserving evaluation of both linear and non-linear operations on the GPU.
Understanding and Mitigating Accuracy Disparity in Regression
With the widespread deployment of large-scale prediction systems in high-stakes domains, e. g., face recognition, criminal justice, etc., disparity in prediction accuracy between different demographic subgroups has called for fundamental understanding on the source of such disparity and algorithmic intervention to mitigate it.
Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Tasks
We study the problem of incorporating prior knowledge into a deep Transformer-based model, i. e., Bidirectional Encoder Representations from Transformers (BERT), to enhance its performance on semantic textual matching tasks.
Meta Federated Learning
Due to its distributed methodology alongside its privacy-preserving features, Federated Learning (FL) is vulnerable to training time adversarial attacks.
SkillBot: Identifying Risky Content for Children in Alexa Skills
Finally, we identify a novel risk in the VPA ecosystem: confounding utterances, or voice commands shared by multiple apps that may cause a user to interact with a different app than intended.
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks.
Intent Classification and Slot Filling for Privacy Policies
We refer to predicting the privacy practice explained in a sentence as intent classification and identifying the text spans sharing specific information as slot filling.
Battery Model Calibration with Deep Reinforcement Learning
In this paper, we implement a Reinforcement Learning-based framework for reliably and efficiently inferring calibration parameters of battery models.
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction
Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem.
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
In comparison with the existing RL algorithms, the proposed method can achieve superior performance in terms of maintaining safety.
PolicyQA: A Reading Comprehension Dataset for Privacy Policies
Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question.
Dual-path CNN with Max Gated block for Text-Based Person Re-identification
With that in mind, a novel Dual-path CNN with Max Gated block (DCMG) is proposed to extract discriminative word embeddings and make visual-textual association concern more on remarkable features of both modalities.
Object Detection in the Context of Mobile Augmented Reality
Ordinary object detection approaches process information from the images only, and they are oblivious to the camera pose with regard to the environment and the scale of the environment.
Perceptron Synthesis Network: Rethinking the Action Scale Variances in Videos
More specifically, an \textit{action perceptron synthesizer} is proposed to generate the kernels from a bag of fixed-size kernels that are interacted by dense routing paths.
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.
Ranked #12 on
Image Generation
on STL-10
Model-Targeted Poisoning Attacks with Provable Convergence
Our attack is the first model-targeted poisoning attack that provides provable convergence for convex models, and in our experiments, it either exceeds or matches state-of-the-art attacks in terms of attack success rate and distance to the target model.
Real-Time Model Calibration with Deep Reinforcement Learning
The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes.
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
In this paper, we propose GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso, which is a nonlinear feature selection method.
$H_\infty$ Model-free Reinforcement Learning with Robust Stability Guarantee
In this paper, we introduce and extend the idea of robust stability and $H_\infty$ control to design policies with both stability and robustness guarantee.
Convex Reconstruction of Structured Matrix Signals from Linear Measurements (I): Theoretical Results
The first part is about conditions for stability and robustness in signal reconstruction via solving the convex programming from noise-free or noisy measurements. We establish uniform sufficient conditions which are very close to necessary conditions and non-uniform conditions are also discussed.
Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee
Reinforcement learning (RL) offers a principled way to achieve the optimal cumulative performance index in discrete-time nonlinear stochastic systems, which are modeled as Markov decision processes.
Adversarial Privacy Preservation under Attribute Inference Attack
With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges.
Variational Constrained Reinforcement Learning with Application to Planning at Roundabout
In this paper, we combine variational learning and constrained reinforcement learning to simultaneously learn a Conditional Representation Model (CRM) to encode the states into safe and unsafe distributions respectively as well as to learn the corresponding safe policy.
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction.
Ranked #6 on
Relation Extraction
on NYT11-HRL
Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction
In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples.
Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries
In a black-box setting, the adversary only has API access to the target model and each query is expensive.
Cryptography and Security
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation
Meanwhile, it is clear that in general there is a tension between minimizing information leakage and maximizing task accuracy.
PatchNet: A Tool for Deep Patch Classification
This work proposes PatchNet, an automated tool based on hierarchical deep learning for classifying patches by extracting features from commit messages and code changes.
Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phase
We present a practical method for protecting data during the inference phase of deep learning based on bipartite topology threat modeling and an interactive adversarial deep network construction.
Understanding and Mitigating the Security Risks of Voice-Controlled Third-Party Skills on Amazon Alexa and Google Home
The significance of our findings have already been acknowledged by Amazon and Google, and further evidenced by the risky skills discovered on Alexa and Google markets by the new detection systems we built.
Cryptography and Security
Query-limited Black-box Attacks to Classifiers
Specifically, we consider the problem of attacking machine learning classifiers subject to a budget of feature modification cost while minimizing the number of queries, where each query returns only a class and confidence score.
WebAPIRec: Recommending Web APIs to Software Projects via Personalized Ranking
In this light, we propose a new, automated approach called WebAPIRec that takes as input a project profile and outputs a ranked list of {web} APIs that can be used to implement the project.
Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be
We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas.
Cryptography and Security
Background Subtraction via Generalized Fused Lasso Foreground Modeling
Background Subtraction (BS) is one of the key steps in video analysis.
