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Found 29 papers, 5 papers with code
Chain of Thought Explanation for Dialogue State Tracking
Dialogue state tracking (DST) aims to record user queries and goals during a conversational interaction achieved by maintaining a predefined set of slots and their corresponding values.
CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations
Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge.
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration
Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing, demonstrating exceptional capabilities in reasoning, tool usage, and memory.
Hyp-UML: Hyperbolic Image Retrieval with Uncertainty-aware Metric Learning
Compared to the conventional Euclidean embedding in most of the previously developed models, Hyperbolic embedding can be more effective in representing the hierarchical data structure.
CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations
Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally.
PhD Learning: Learning With Pompeiu-Hausdorff Distances for Video-Based Vehicle Re-Identification
Vehicle re-identification (re-ID) is of great significance to urban operation, management, security and has gained more attention in recent years.
2nd Place Solution for IJCAI-PRICAI 2020 3D AI Challenge: 3D Object Reconstruction from A Single Image
In this paper, we present our solution for the {\it IJCAI--PRICAI--20 3D AI Challenge: 3D Object Reconstruction from A Single Image}.
EMface: Detecting Hard Faces by Exploring Receptive Field Pyraminds
Modern face detectors employ feature pyramids to deal with scale variation.
IDEAL: Independent Domain Embedding Augmentation Learning
Our IDEAL with MS loss also achieves the new state-of-the-art performance on three image retrieval benchmarks, \ie, \emph{Cars-196}, \emph{CUB-200}, and \emph{SOP}.
Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues
It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query.
Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review
Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive Model
Unifying Relational Sentence Generation and Retrieval for Medical Image Report Composition
Beyond generating long and topic-coherent paragraphs in traditional captioning tasks, the medical image report composition task poses more task-oriented challenges by requiring both the highly-accurate medical term diagnosis and multiple heterogeneous forms of information including impression and findings.
Diversity Augmented Conditional Generative Adversarial Network for Enhanced Multimodal Image-to-Image Translation
We propose Diversity Augmented conditional Generative Adversarial Network (DivAugGAN), a highly effective solution to further resolve the mode collapse problem and enhance the diversity for the generated images.
Generative Adversarial Network
Image-to-Image Translation
+1
Towards a Reliable and Robust Dialogue System for Medical Automatic Diagnosis
To this end, we propose a novel DSMAD agent, INS-DS (Introspective Diagnosis System) comprising of two separate yet cooperative modules, i. e., an inquiry module for proposing symptom-inquiries and an introspective module for deciding when to inform a disease.
Polarimetric SAR Image Semantic Segmentation with 3D Discrete Wavelet Transform and Markov Random Field
Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications.
A Preliminary Study on Optimal Placement of Cameras
This paper primarily focuses on figuring out the best array of cameras, or visual sensors, so that such a placement enables the maximum utilization of these visual sensors.
Detection and Classification of Breast Cancer Metastates Based on U-Net
This paper presents U-net based breast cancer metastases detection and classification in lymph nodes, as well as patient-level classification based on metastases detection.
DaTscan SPECT Image Classification for Parkinson's Disease
Human visual analysis is slow and vulnerable to subjectivity between radiologists, so the goal was to develop an introductory implementation of a deep convolutional neural network that can objectively and accurately classify DaTscan SPECT images as Parkinson's Disease or normal.
HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning
Then the proposed HorNet can learn the visual and language representation from both the images and captions jointly, and thus enhance the performance of person re-ID.
Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition
It can accelerate the convergence rate significantly while achieving a state-of-the-art recognition performance.
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
Besides the challenges for conversational dialogue systems (e. g. topic transition coherency and question understanding), automatic medical diagnosis further poses more critical requirements for the dialogue rationality in the context of medical knowledge and symptom-disease relations.
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework.
Ranked #13 on
Hyperspectral Image Classification
on Indian Pines
(Overall Accuracy metric, using extra
training data)
Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
The weights in the other group are responsible to compensate for the accuracy loss from the quantization, thus they are the ones to be re-trained.
Greedy Criterion in Orthogonal Greedy Learning
In this paper, we find that SGD is not the unique greedy criterion and introduce a new greedy criterion, called "$\delta$-greedy threshold" for learning.
Re-scale AdaBoost for Attack Detection in Collaborative Filtering Recommender Systems
Collaborative filtering recommender systems (CFRSs) are the key components of successful e-commerce systems.
Shrinkage degree in $L_2$-re-scale boosting for regression
Re-scale boosting (RBoosting) is a variant of boosting which can essentially improve the generalization performance of boosting learning.
Re-scale boosting for regression and classification
Boosting is a learning scheme that combines weak prediction rules to produce a strong composite estimator, with the underlying intuition that one can obtain accurate prediction rules by combining "rough" ones.
Greedy metrics in orthogonal greedy learning
Orthogonal greedy learning (OGL) is a stepwise learning scheme that adds a new atom from a dictionary via the steepest gradient descent and build the estimator via orthogonal projecting the target function to the space spanned by the selected atoms in each greedy step.
Algorithm Runtime Prediction: Methods & Evaluation
We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems.
