Search Results for author:
Found 36 papers, 8 papers with code
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks.
AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning
With the rapid adoption of machine learning (ML), a number of domains now use the approach of fine tuning models which were pre-trained on a large corpus of data.
Knowledge Crosswords: Geometric Reasoning over Structured Knowledge with Large Language Models
We additionally propose two new approaches, Staged Prompting and Verify-All, to augment LLMs' ability to backtrack and verify structured constraints.
CacheGen: KV Cache Compression and Streaming for Fast Language Model Serving
Compared to the recent systems that reuse the KV cache, CacheGen reduces the KV cache size by 3. 7-4. 3x and the total delay in fetching and processing contexts by 2. 7-3. 2x while having negligible impact on the LLM response quality in accuracy or perplexity.
Entanglement-guided architectures of machine learning by quantum tensor network
In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by many-qubit quantum states written in the matrix product states (MPS).
Empathetic Response Generation with State Management
The emotion-aware dialogue management contains two parts: (1) Emotion state tracking maintains the current emotion state of the user and (2) Empathetic dialogue policy selection predicts a target emotion and a user's intent based on the results of the emotion state tracking.
Learning Continuous Control Policies for Information-Theoretic Active Perception
This paper proposes a method for learning continuous control policies for active landmark localization and exploration using an information-theoretic cost.
Learning discrete distributions: user vs item-level privacy
If each user has $m$ samples, we show that straightforward applications of Laplace or Gaussian mechanisms require the number of users to be $\mathcal{O}(k/(m\alpha^2) + k/\epsilon\alpha)$ to achieve an $\ell_1$ distance of $\alpha$ between the true and estimated distributions, with the privacy-induced penalty $k/\epsilon\alpha$ independent of the number of samples per user $m$.
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
Auto-calibration Method Using Stop Signs for Urban Autonomous Driving Applications
Calibration of sensors is fundamental to robust performance for intelligent vehicles.
Estimating Sparse Discrete Distributions Under Local Privacy and Communication Constraints
We consider the problem of estimating sparse discrete distributions under local differential privacy (LDP) and communication constraints.
Tidal surface states as fingerprints of non-Hermitian nodal knot metals
Non-Hermitian nodal knot metals (NKMs) contains intricate complex-valued energy bands gives rise to knotted exceptional loops and new topological surface states.
Mesoscale and Nanoscale Physics Materials Science Other Condensed Matter Mathematical Physics Mathematical Physics Quantum Physics
Accelerating Deep Learning Inference via Learned Caches
We present the design of GATI, an end-to-end prediction serving system that incorporates learned caches for low-latency DNN inference.
Textual Analysis of Communications in COVID-19 Infected Community on Social Media
During the COVID-19 pandemic, people started to discuss about pandemic-related topics on social media.
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
Multi defect detection and analysis of electron microscopy images with deep learning
Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis.
Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs
In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model.
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations
The emotion cause is a stimulus for human emotions.
基于循环交互注意力网络的问答立场分析(A Recurrent Interactive Attention Network for Answer Stance Analysis)
针对问答立场任务中, 现有方法难以提取问答文本间的依赖关系问题, 本文提出一种基于循环交互注意力(Recurrent Interactive Attention, RIA)网络的问答立场分析方法。该方法通过模仿人类阅读理解时的思维方式, 基于交互注意力机制和循环迭代方法, 有效地从问题和答案的相互联系中挖掘问答文本的立场信息。此外, 该方法将问题进行陈述化表示, 有效地解决疑问句表述下问题文本无法明确表达自身立场的问题。实验结果表明, 本文方法取得了比现有模型方法更好的效果, 同时证明该方法能有效拟合问答立场分析任务中的问答对依赖关系。
Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition
We obtain tight minimax rates for the problem of distributed estimation of discrete distributions under communication constraints, where $n$ users observing $m $ samples each can broadcast only $\ell$ bits.
Learning Based Model Predictive Control for Quadcopters with Dual Gaussian Process
In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control strategy that improves the performance of a quadcopter during trajectory tracking.
Human Behavior Recognition Method Based on CEEMD-ES Radar Selection
First, this method decomposes and reconstructs the radar signal according to the difference in the reflected echo frequency between the limbs and the trunk of the human body.
Algorithms for bounding contribution for histogram estimation under user-level privacy
In this scenario, the amount of noise injected into the histogram to obtain differential privacy is proportional to the maximum user contribution, which can be amplified by few outliers.
Discrete Distribution Estimation under User-level Local Differential Privacy
Perhaps surprisingly, we show that in suitable parameter regimes, having $m$ samples per user is equivalent to having $m$ times more users, each with only one sample.
Learning For Predictive Control: A Dual Gaussian Process Approach
Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data.
Agent-based Simulation for Online Mental Health Matching
Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues.
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model
In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model.
GRACE: Loss-Resilient Real-Time Video through Neural Codecs
In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements.
OneAdapt: Fast Adaptation for Deep Learning Applications via Backpropagation
While the high demand for network bandwidth and GPU resources could be substantially reduced by optimally adapting the configuration knobs, such as video resolution and frame rate, current adaptation techniques fail to meet three requirements simultaneously: adapt configurations (i) with minimum extra GPU or bandwidth overhead; (ii) to reach near-optimal decisions based on how the data affects the final DNN's accuracy, and (iii) do so for a range of configuration knobs.
Automatic and Efficient Customization of Neural Networks for ML Applications
ML APIs have greatly relieved application developers of the burden to design and train their own neural network models -- classifying objects in an image can now be as simple as one line of Python code to call an API.
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning
One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task.
P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models
In this work, we take a first step towards designing summarization systems that are faithful to the author's intent, not only the semantic content of the article.
Chatterbox: Robust Transport for LLM Token Streaming under Unstable Network
To render each generated token in real time, the LLM server generates response tokens one by one and streams each generated token (or group of a few tokens) through the network to the user right after it is generated, which we refer to as LLM token streaming.
MMoE: Robust Spoiler Detection with Multi-modal Information and Domain-aware Mixture-of-Experts
For instance, the metadata and the corresponding user's information of a review could be helpful.
From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News
Our simulation results uncover patterns in fake news propagation related to topic relevance, and individual traits, aligning with real-world observations.
