Search Results for author:
Found 35 papers, 10 papers with code
Automatic Evaluation for Mental Health Counseling using LLMs
High-quality psychological counseling is crucial for mental health worldwide, and timely evaluation is vital for ensuring its effectiveness.
Unveiling the Secrets of Engaging Conversations: Factors that Keep Users Hooked on Role-Playing Dialog Agents
With the growing humanlike nature of dialog agents, people are now engaging in extended conversations that can stretch from brief moments to substantial periods of time.
FlowZero: Zero-Shot Text-to-Video Synthesis with LLM-Driven Dynamic Scene Syntax
Text-to-video (T2V) generation is a rapidly growing research area that aims to translate the scenes, objects, and actions within complex video text into a sequence of coherent visual frames.
ActiveDC: Distribution Calibration for Active Finetuning
Firstly, we select samples for annotation by optimizing the distribution similarity between the subset to be selected and the entire unlabeled pool in continuous space.
Data-Driven Modeling of an Unsaturated Bentonite Buffer Model Test Under High Temperatures Using an Enhanced Axisymmetric Reproducing Kernel Particle Method
In deep geological repositories for high level nuclear waste with close canister spacings, bentonite buffers can experience temperatures higher than 100 {\deg}C. In this range of extreme temperatures, phenomenological constitutive laws face limitations in capturing the thermo-hydro-mechanical (THM) behavior of the bentonite, since the pre-defined functional constitutive laws often lack generality and flexibility to capture a wide range of complex coupling phenomena as well as the effects of stress state and path dependency.
NEOLAF, an LLM-powered neural-symbolic cognitive architecture
This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents.
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets
High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design.
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction
In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner.
Rediscovery of Numerical Lüscher's Formula from the Neural Network
We present that by predicting the spectrum in discrete space from the phase shift in continuous space, the neural network can remarkably reproduce the numerical L\"uscher's formula to a high precision.
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives
This dynamically personalized FL technique incentivizes clients to participate in personalizing local models while allowing the adoption of the global model when it performs better.
A Simple yet Effective Framework for Active Learning to Rank
To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understanding users' queries, retrieve relevant contents from a pool of trillions of webpages, and rank the most relevant webpages on the top of results.
Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network
Machine learning-based methods have achieved successful applications in machinery fault diagnosis.
Learning Confidence for Transformer-based Neural Machine Translation
Confidence estimation aims to quantify the confidence of the model prediction, providing an expectation of success.
Federated Two-stage Learning with Sign-based Voting
In this paper, we design a federated two-stage learning framework that augments prototypical federated learning with a cut layer on devices and uses sign-based stochastic gradient descent with the majority vote method on model updates.
CRIS: CLIP-Driven Referring Image Segmentation
In addition, we present text-to-pixel contrastive learning to explicitly enforce the text feature similar to the related pixel-level features and dissimilar to the irrelevances.
Contrastive Learning
Generalized Referring Expression Segmentation
+3
Interpreting Deep Knowledge Tracing Model on EdNet Dataset
With more deep learning techniques being introduced into the knowledge tracing domain, the interpretability issue of the knowledge tracing models has aroused researchers' attention.
Attention Calibration for Transformer in Neural Machine Translation
Attention mechanisms have achieved substantial improvements in neural machine translation by dynamically selecting relevant inputs for different predictions.
Towards Heterogeneous Clients with Elastic Federated Learning
Training in heterogeneous and potentially massive networks introduces bias into the system, which is originated from the non-IID data and the low participation rate in reality.
Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention
Emotion recognition from speech is a challenging task.
RevCore: Review-augmented Conversational Recommendation
Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items.
RoFormer: Enhanced Transformer with Rotary Position Embedding
Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information.
Ranked #1 on
Semantic Text Matching
on CAIL2019-SCM - test
GINet: Graph Interaction Network for Scene Parsing
GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph.
CASIA's System for IWSLT 2020 Open Domain Translation
This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task.
Towards Interpretable Deep Learning Models for Knowledge Tracing
We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models.
Object Instance Mining for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) using only image-level annotations has attracted growing attention over the past few years.
Ranked #10 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
C-DLinkNet: considering multi-level semantic features for human parsing
Human parsing is an essential branch of semantic segmentation, which is a fine-grained semantic segmentation task to identify the constituent parts of human.
Learning Efficient Video Representation with Video Shuffle Networks
To this end, we propose the video shuffle, a parameter-free plug-in component that efficiently reallocates the inputs of 2D convolution so that its receptive field can be extended to the temporal dimension.
Structured Pruning for Efficient ConvNets via Incremental Regularization
Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance degrade.
Provably Optimal Algorithms for Generalized Linear Contextual Bandits
Contextual bandits are widely used in Internet services from news recommendation to advertising, and to Web search.
Statistical and Computational Guarantees of Lloyd's Algorithm and its Variants
Lloyd's algorithm, proposed in 1957, is still possibly the most widely used clustering algorithm in practice due to its simplicity and empirical performance.
Exact Exponent in Optimal Rates for Crowdsourcing
In many machine learning applications, crowdsourcing has become the primary means for label collection.
Optimal Estimation and Completion of Matrices with Biclustering Structures
Biclustering structures in data matrices were first formalized in a seminal paper by John Hartigan (1972) where one seeks to cluster cases and variables simultaneously.
Multi-Linear Interactive Matrix Factorization
Recommender systems, which can significantly help users find their interested items from the information era, has attracted an increasing attention from both the scientific and application society.
Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters
On real spectra, we extracted 23 features to estimate $T_{eff}$, 62 features for log$~g$, and 68 features for [Fe/H].
Individualized Rank Aggregation using Nuclear Norm Regularization
In recent years rank aggregation has received significant attention from the machine learning community.
