Search Results for author:
Found 24 papers, 8 papers with code
Benchmarking Language-agnostic Intent Classification for Virtual Assistant Platforms
Current virtual assistant (VA) platforms are beholden to the limited number of languages they support.
CREATOR: Disentangling Abstract and Concrete Reasonings of Large Language Models through Tool Creation
Large Language Models (LLMs) have demonstrated significant progress in utilizing external APIs as tools for various tasks.
Recyclable Tuning for Continual Pre-training
In pilot studies, we find that after continual pre-training, the upgraded PLM remains compatible with the outdated adapted weights to some extent.
DeCom: Deep Coupled-Factorization Machine for Post COVID-19 Respiratory Syncytial Virus Prediction with Nonpharmaceutical Interventions Awareness
Respiratory syncytial virus (RSV) is one of the most dangerous respiratory diseases for infants and young children.
Tool Learning with Foundation Models
Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 17 representative tools and show the potential of current foundation models in skillfully utilizing tools.
One-Bit Covariance Reconstruction with Non-zero Thresholds: Algorithm and Performance Analysis
To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds.
Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants
Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query.
Exploring Mode Connectivity for Pre-trained Language Models
(3) How does the PLM's task knowledge change along the path connecting two minima?
GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion
Our variant GOCPTE shows up to 1:2% and 5:5% fitness improvement on two datasets with about 20% speedup compared to the best model.
Clinical trial site matching with improved diversity using fair policy learning
More recently, the issue of diversity and inclusion in clinical trials is gaining importance.
JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion
Tensor completion aims at imputing missing entries from a partially observed tensor.
ATD: Augmenting CP Tensor Decomposition by Self Supervision
This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification.
MTC: Multiresolution Tensor Completion from Partial and Coarse Observations
Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares.
Condition Integration Memory Network: An Interpretation of the Meaning of the Neuronal Design
When a neuron's activation represents some symbolic element in the environment, each of its synapses can indicate a potential change to the element and its future state.
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data
Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem.
One-Bit Target Detection in Collocated MIMO Radar and Performance Degradation Analysis
Target detection is an important problem in multiple-input multiple-output (MIMO) radar.
STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures.
Learning Barrier Functions with Memory for Robust Safe Navigation
Control barrier functions are widely used to enforce safety properties in robot motion planning and control.
Motion Planning
Robotics
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors
In particular, we define the N-th order tensor Wasserstein loss for the widely used tensor CP factorization and derive the optimization algorithm that minimizes it.
On the Compression of Translation Operator Tensors in FMM-FFT-Accelerated SIE Simulators via Tensor Decompositions
Tensor decomposition methodologies are proposed to reduce the memory requirement of translation operator tensors arising in the fast multipole method-fast Fourier transform (FMM-FFT)-accelerated surface integral equation (SIE) simulators.
Model-aided Deep Neural Network for Source Number Detection
Specifically, we first propose the eigenvalue based regression network (ERNet) and classification network (ECNet) to estimate the number of non-coherent sources, where the eigenvalues of the received signal covariance matrix and the source number are used as the input and the supervise label of the networks, respectively.
REP: Predicting the Time-Course of Drug Sensitivity
Time-course gene expression data is a rich source of information that can be used to unravel these complex processes, identify biomarkers of drug sensitivity and predict the response to a drug.
High-dimensional Gaussian graphical model for network-linked data
Graphical models are commonly used to represent conditional dependence relationships between variables.
From Gene Expression to Drug Response: A Collaborative Filtering Approach
Predicting the response of cancer cells to drugs is an important problem in pharmacogenomics.
