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Found 61 papers, 22 papers with code
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data.
KPT: Keyword-guided Pre-training for Grounded Dialog Generation
For the former, the grounding knowledge consists of keywords extracted from the response.
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.
An Overview on Controllable Text Generation via Variational Auto-Encoders
Recent advances in neural-based generative modeling have reignited the hopes of having computer systems capable of conversing with humans and able to understand natural language.
CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature.
Whole-System First-Swing Stability of Inverter-Based Inertia-Free Power Systems
The emphasis on inertia for system stability has been a long-held tradition in conventional grids.
PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects
Object pose estimation is crucial for robotic applications and augmented reality.
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression
Depth estimation is a core task in 3D computer vision.
fairlib: A Unified Framework for Assessing and Improving Classification Fairness
This paper presents fairlib, an open-source framework for assessing and improving classification fairness.
Impedance-based Root-cause Analysis: Comparative Study of Impedance Models and Calculation of Eigenvalue Sensitivity
Impedance models of power systems are useful when state-space models of apparatus such as inverter-based resources (IBRs) have not been made available and instead only black-box impedance models are available.
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model
We investigate different aspects of responses generated by PanGu-Bot, including response quality, knowledge, and safety.
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion
Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention.
Compilable Neural Code Generation with Compiler Feedback
Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering.
Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks
The research of open-domain dialog systems has been greatly prospered by neural models trained on large-scale corpora, however, such corpora often introduce various safety problems (e. g., offensive languages, biases, and toxic behaviors) that significantly hinder the deployment of dialog systems in practice.
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation
Experiments over publicly available datasets demonstrate that our method can help models generate better responses, even such training data are usually impressed as low-quality data.
UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues
With the advances in deep learning, tremendous progress has been made with chit-chat dialogue systems and task-oriented dialogue systems.
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems
As labeling cost for different modules in task-oriented dialog (ToD) systems is high, a major challenge in practice is to learn different tasks with the least amount of labeled data.
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training
In this work, we propose an approach, MultiUAT, that dynamically adjusts the training data usage based on the model's uncertainty on a small set of trusted clean data for multi-corpus machine translation.
Revisiting Grid-Forming and Grid-Following Inverters: A Duality Theory
Power electronic converters for integrating renewable energy resources into power systems can be divided into grid-forming and grid-following inverters.
Mapping of Dynamics between Mechanical and Electrical Ports in SG-IBR Composite Grids
The SG-dominated grid is traditionally analyzed in a mechanical-centric view which ignores fast electrical dynamics and focuses on the torque-speed dynamics.
The Intrinsic Communication in Power Systems: A New Perspective to Understand Synchronization Stability
Based on this isomorphism, we revisit power system synchronization stability from a communication perspective and thereby establish a theory that unifies the synchronization dynamics of heterogeneous power apparatuses.
Automated Generation of Interorganizational Disaster Response Networks through Information Extraction
When a disaster occurs, maintaining and restoring community lifelines subsequently require collective efforts from various stakeholders.
Participation Analysis in Impedance Models: The Grey-Box Approach for Power System Stability
This paper develops a grey-box approach to small-signal stability analysis of complex power systems that facilitates root-cause tracing without requiring disclosure of the full details of the internal control structure of apparatus connected to the system.
Polarized hyperspectral imaging with single fiber bundle via incoherent light transmission matrix approach
The scattering of multispectral incoherent light is a common and unfavorable signal scrambling in natural scenes.
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
It has been demonstrated that hidden representation learned by a deep model can encode private information of the input, hence can be exploited to recover such information with reasonable accuracy.
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy.
CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair
To address these challenges, we propose a new G&V technique—CoCoNuT, which uses ensemble learning on the combination of convolutional neural networks (CNNs) and a new context-aware neural machine translation (NMT) architecture to automatically fix bugs in multiple programming languages.
Towards Differentially Private Text Representations
Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model.
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance
Learning disentangled representations of natural language is essential for many NLP tasks, e. g., conditional text generation, style transfer, personalized dialogue systems, etc.
Automated Abstraction of Operation Processes from Unstructured Text for Simulation Modeling
Overall, this research enhances the state-of-the-art simulation modeling through achieving automated abstraction of operation processes, which largely reduces modelers' interpretation load and ensures the reliability of the abstracted operation processes.
Toward Interpretability of Dual-Encoder Models for Dialogue Response Suggestions
To improve the interpretability in the dual encoder models, we design a novel regularization loss to minimize the mutual information between unimportant words and desired labels, in addition to the original attention method, so that important words are emphasized while unimportant words are de-emphasized.
Estimating Uncertainty Intervals from Collaborating Networks
We address these challenges by proposing a novel method to capture predictive distributions in regression by defining two neural networks with two distinct loss functions.
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally.
Dynamic Embedding on Textual Networks via a Gaussian Process
The relative importance of global versus local structure for the embeddings is learned automatically.
LMVP: Video Predictor with Leaked Motion Information
We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs.
Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks
This paper aims to propose a novel deep learning-integrated framework for deriving reliable simulation input models through incorporating multi-source information.
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference
Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting.
Towards Fair and Privacy-Preserving Federated Deep Models
This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates.
On Target Shift in Adversarial Domain Adaptation
In this work, we propose a method called Domain Adversarial nets for Target Shift (DATS) to address label shift while learning a domain invariant representation.
Sequential Attention GAN for Interactive Image Editing
The main challenges in this sequential and interactive image generation task are two-fold: 1) contextual consistency between a generated image and the provided textual description; 2) step-by-step region-level modification to maintain visual consistency across the generated image sequence in each session.
StoryGAN: A Sequential Conditional GAN for Story Visualization
We therefore propose a new story-to-image-sequence generation model, StoryGAN, based on the sequential conditional GAN framework.
Extracting Relationships by Multi-Domain Matching
This methodology builds on existing distribution-matching approaches by assuming that source domains are varied and outcomes multi-factorial.
Diffusion Maps for Textual Network Embedding
Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices.
PiPs: a Kernel-based Optimization Scheme for Analyzing Non-Stationary 1D Signals
This paper proposes a novel kernel-based optimization scheme to handle tasks in the analysis, e. g., signal spectral estimation and single-channel source separation of 1D non-stationary oscillatory data.
What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training
Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (in domain) and dissimilar (out of domain) instances to those seen in training.
Towards Robust and Privacy-preserving Text Representations
Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes.
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector.
Targeting EEG/LFP Synchrony with Neural Nets
We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP) datasets, which are “big” in terms of the size of recorded data but rarely have sufficient labels required to train complex models (e. g., conventional deep learning methods).
Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering
This paper proposes an uncertain data clustering approach to quantitatively analyze the complexity of prefabricated construction components through the integration of quality performance-based measures with associated engineering design information.
Learning Context-Sensitive Convolutional Filters for Text Processing
The role of meta network is to abstract the contextual information of a sentence or document into a set of input-aware filters.
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BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning
This paper describes our submission to the sentiment analysis sub-task of {``}Build It, Break It: The Language Edition (BIBI){''}, on both the builder and breaker sides.
Robust Training under Linguistic Adversity
Deep neural networks have achieved remarkable results across many language processing tasks, however they have been shown to be susceptible to overfitting and highly sensitive to noise, including adversarial attacks.
Learning Robust Representations of Text
Deep neural networks have achieved remarkable results across many language processing tasks, however these methods are highly sensitive to noise and adversarial attacks.
EventNet: A Large Scale Structured Concept Library for Complex Event Detection in Video
Extensive experiments over the zero-shot event retrieval task when no training samples are available show that the EventNet concept library consistently and significantly outperforms the state-of-the-art (such as the 20K ImageNet concepts trained with CNN) by a large margin up to 207%.
