Search Results for author:
Found 26 papers, 6 papers with code
An Analytic Solution for Kernel Adaptive Filtering
We provide an extended functional Wiener equation, and present a solution to this equation in an explicit, finite dimensional, data-dependent RKHS.
Generative Dense Retrieval: Memory Can Be a Burden
Memorizing-free matching mechanism from Dense Retrieval (DR) is then introduced to conduct fine-grained intra-cluster matching from clusters to relevant documents.
BatchEval: Towards Human-like Text Evaluation
Significant progress has been made in automatic text evaluation with the introduction of large language models (LLMs) as evaluators.
Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data
In this work, we illustrate the merit of negative data and propose a model specialization framework to distill LLMs with negative samples besides positive ones.
EXMODD: An EXplanatory Multimodal Open-Domain Dialogue dataset
The need for high-quality data has been a key issue hindering the research of dialogue tasks.
Bilevel Scheduled Sampling for Dialogue Generation
To enhance sampling diversity and improve the model's adaptability, we propose a smooth function that maps the combined result of sentence-level and word-level information to an appropriate range, and employ probabilistic sampling based on the mapped values instead of threshold truncation.
Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey
Multi-task learning has been widely applied in computational vision, natural language processing and other fields, which has achieved well performance.
Heterogeneous-Branch Collaborative Learning for Dialogue Generation
Collaborative learning, also known as online knowledge distillation, is an effective way to conduct one-stage group distillation in the absence of a well-trained large teacher model.
Towards Diverse, Relevant and Coherent Open-Domain Dialogue Generation via Hybrid Latent Variables
Specifically, HLV constrains the global semantics of responses through discrete latent variables and enriches responses with continuous latent variables.
Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder
Complex dialogue mappings (CDM), including one-to-many and many-to-one mappings, tend to make dialogue models generate incoherent or dull responses, and modeling these mappings remains a huge challenge for neural dialogue systems.
Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning
However, the discarded samples may obtain high scores in other perspectives and can provide regularization effects on the model learning, which causes the performance improvement to be sensitive to the filtering ratio.
Diversifying Neural Dialogue Generation via Negative Distillation
Generative dialogue models suffer badly from the generic response problem, limiting their applications to a few toy scenarios.
Hierarchical Inductive Transfer for Continual Dialogue Learning
However, for the continual increase of online chit-chat scenarios, directly fine-tuning these models for each of the new tasks not only explodes the capacity of the dialogue system on the embedded devices but also causes knowledge forgetting on pre-trained models and knowledge interference among diverse dialogue tasks.
Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders
Conditional Variational AutoEncoder (CVAE) effectively increases the diversity and informativeness of responses in open-ended dialogue generation tasks through enriching the context vector with sampled latent variables.
THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses
In this work, we proposed a conversation model named "THINK" (Teamwork generation Hover around Impressive Noticeable Keywords) to make the decoder more complicated and avoid generating duplicated and self-contradicting responses.
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
The finding of general knowledge is further hindered by the unidirectional distillation, as the student should obey the teacher and may discard some knowledge that is truly general but refuted by the teacher.
Regularizing Dialogue Generation by Imitating Implicit Scenarios
Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario.
Collaborative Group Learning
Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima.
Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
Neural conversational models learn to generate responses by taking into account the dialog history.
Fast Estimation of Information Theoretic Learning Descriptors using Explicit Inner Product Spaces
The inner product defined by the feature mapping corresponds to a positive-definite finite-rank kernel that induces a finite-dimensional reproducing kernel Hilbert space (RKHS).
No-Trick (Treat) Kernel Adaptive Filtering using Deterministic Features
Without loss of generality, we apply this approach to classical adaptive filtering algorithms and validate the methodology to show that deterministic features are faster to generate and outperform state-of-the-art kernel methods based on random Fourier features.
Functional Bayesian Filter
We present a general nonlinear Bayesian filter for high-dimensional state estimation using the theory of reproducing kernel Hilbert space (RKHS).
Behavior Pattern and Compiled Information Based Performance Prediction in MOOCs
This paper mainly focuses on the students' accomplishment behaviors in programming assignments and compiled information from programming assignments.
Magnitude Bounded Matrix Factorisation for Recommender Systems
Low rank matrix factorisation is often used in recommender systems as a way of extracting latent features.
Relative Pairwise Relationship Constrained Non-negative Matrix Factorisation
Non-negative Matrix Factorisation (NMF) has been extensively used in machine learning and data analytics applications.
