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Found 68 papers, 26 papers with code
NICE: Neural Image Commenting with Empathy
Based on these annotations, we define two different tasks for the NICE dataset.
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Motivated by this gap, we introduce a novel UQ method, sampling with perturbation for UQ (SPUQ), designed to tackle both aleatoric and epistemic uncertainties.
Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing
Subsequently, we transform the point cloud using the latent poses, feeding it to the part encoder for aggregating super-part information and reasoning about part relationships to predict all part poses.
Investigating White-Box Attacks for On-Device Models
Our findings emphasize the need for developers to carefully consider their model deployment strategies, and use white-box methods to evaluate the vulnerability of on-device models.
Customizing Language Model Responses with Contrastive In-Context Learning
However, it can be challenging to align LLMs with our intent, particularly when we want to generate content that is preferable over others or when we want the LLM to respond in a certain style or tone that is hard to describe.
InvariantOODG: Learning Invariant Features of Point Clouds for Out-of-Distribution Generalization
The convenience of 3D sensors has led to an increase in the use of 3D point clouds in various applications.
Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions
One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport.
Cooperative Dispatch of Microgrids Community Using Risk-Sensitive Reinforcement Learning with Monotonously Improved Performance
This online algorithm enables MGs to spontaneously search for the Pareto Frontier considering multiple objectives and risk mitigation.
Colmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration
In contrast, mapping methods based on LiDAR scans are popular in large-scale urban scene reconstruction due to their precise distance measurements, a capability fundamentally absent in visual-based approaches.
Incremental Rotation Averaging Revisited and More: A New Rotation Averaging Benchmark
In addition, to further address the limitations of the existing rotation averaging benchmark of relying on the slightly outdated Bundler camera calibration results as ground truths and focusing solely on rotation estimation accuracy, this paper presents a new COLMAP-based rotation averaging benchmark that incorporates a cross check between COLMAP and Bundler, and employ the accuracy of both rotation and downstream location estimation as evaluation metrics, which is desired to provide a more reliable and comprehensive evaluation tool for the rotation averaging research.
A Quick Guide for the Iterated Extended Kalman Filter on Manifolds
In contrast, the iterated EKF (IEKF) refines the state in the update step by iteratively solving a least squares problem.
Modularizing while Training: A New Paradigm for Modularizing DNN Models
In this paper, we propose a novel approach that incorporates modularization into the model training process, i. e., modularizing-while-training (MwT).
Open Set Relation Extraction via Unknown-Aware Training
Inspired by text adversarial attacks, we adaptively apply small but critical perturbations to original training instances and thus synthesizing negative instances that are more likely to be mistaken by the model as known relations.
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Model
Pretrained language models have achieved remarkable success in various natural language processing tasks.
Reusing Deep Neural Network Models through Model Re-engineering
Prior approaches to DNN model reuse have two main limitations: 1) reusing the entire model, while only a small part of the model's functionalities (labels) are required, would cause much overhead (e. g., computational and time costs for inference), and 2) model reuse would inherit the defects and weaknesses of the reused model, and hence put the new system under threats of security attack.
Learning Regularized Positional Encoding for Molecular Prediction
To model the complex nonlinearity in predicting molecular properties in an more end-to-end approach, we propose to encode the positional quantities with a learnable embedding that is continuous and differentiable.
Supervised Pretraining for Molecular Force Fields and Properties Prediction
Experiments show that, compared to training from scratch, fine-tuning the pretrained model can significantly improve the performance for seven molecular property prediction tasks and two force field tasks.
Learning Latent Part-Whole Hierarchies for Point Clouds
The decoder takes the latent variable and the feature from the encoder as an input and predicts the per-point part distribution at the top level.
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER
In this work, we conduct an empirical study on the "Unlabeled Entity Problem" and find that it leads to severe confusion between "O" and entities, decreasing class discrimination of old classes and declining the model's ability to learn new classes.
Few-Shot Model Agnostic Federated Learning
To address these issues, this paper presents a novel framework with two main parts: 1) model agnostic federated learning, it performs public-private communication by unifying the model prediction outputs on the shared public datasets; 2) latent embedding adaptation, it addresses the domain gap with an adversarial learning scheme to discriminate the public and private domains.
Patching Weak Convolutional Neural Network Models through Modularization and Composition
To patch a weak CNN model that performs unsatisfactorily on a target class (TC), we compose the weak CNN model with the corresponding module obtained from a strong CNN model.
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e. g., clear edges, smooth color shading, abstract fine structures, etc.).
Multi-view Feature Augmentation with Adaptive Class Activation Mapping
We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance.
Optimal annuitization post-retirement with labor income
In this paper, we study the problem of post-retirement annuitization with extra labor income in the framework of stochastic control, optimal stopping, and expected utility maximization.
SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception
In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance across different images that share common semantic regions.
A Computer-Aided Diagnosis System for Breast Pathology: A Deep Learning Approach with Model Interpretability from Pathological Perspective
Objective: We develop a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer.
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning
Point clouds have attracted increasing attention.
Self-Supervised Graph Representation Learning via Topology Transformations
We formalize the proposed model from an information-theoretic perspective, by maximizing the mutual information between topology transformations and node representations before and after the transformations.
RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.
An Adversarially-Learned Turing Test for Dialog Generation Models
To alleviate this risk, we propose an adversarial training approach to learn a robust model, ATT (Adversarial Turing Test), that discriminates machine-generated responses from human-written replies.
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations
Then, we self-train a representation to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after the transformation.
Pion form factor and charge radius from Lattice QCD at physical point
We study the form factor at the physical point with a lattice spacing $a=0. 076$ fm.
High Energy Physics - Lattice High Energy Physics - Experiment High Energy Physics - Phenomenology Nuclear Theory
Towards studying the structural differences between the pion and its radial excitation
We present an exploratory lattice QCD investigation of the differences between the valence quark structure of pion and its radial excitation $\pi(1300)$ in a fixed finite volume using the leading-twist factorization approach.
High Energy Physics - Lattice High Energy Physics - Experiment High Energy Physics - Phenomenology Nuclear Theory
TopoTER: Unsupervised Learning of Topology Transformation Equivariant Representations
We formalize the TopoTER from an information-theoretic perspective, by maximizing the mutual information between topology transformations and node representations before and after the transformations.
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer).
Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration
We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees such as stability and optimality at systems level.
Towards Expedited Impedance Tuning of a Robotic Prosthesis for Personalized Gait Assistance by Reinforcement Learning Control
Personalizing medical devices such as lower limb wearable robots is challenging.
MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform
We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation.
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance
In particular, we define a manifold-to-manifold distance and its discrete counterpart on graphs to measure the variation-based intrinsic distance between surface patches in the temporal domain, provided that graph operators are discrete counterparts of functionals on Riemannian manifolds.
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations
Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for non-Euclidean data on graphs, which often require a large amount of labeled data with high cost.
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks
In particular, we cast the graph optimization problem as distance metric learning to capture pairwise similarities of features in each layer.
Structuring Latent Spaces for Stylized Response Generation
This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level.
Feature Graph Learning for 3D Point Cloud Denoising
In this work, we assume instead the availability of a relevant feature vector $\mathbf{f}_i$ per node $i$, from which we compute an optimal feature graph via optimization of a feature metric.
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling
The Intelligent Conversation Engine: Code and Pre-trained Systems (Microsoft Icecaps) is an upcoming open-source natural language processing repository.
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous.
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning
Finally, based on the spatial-temporal graph learning, we formulate dynamic point cloud denoising as the joint optimization of the desired point cloud and underlying spatio-temporal graph, which leverages both intra-frame affinities and inter-frame consistency and is solved via alternating minimization.
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification
In this paper, we propose Graph Learning Neural Networks (GLNNs), which exploit the optimization of graphs (the adjacency matrix in particular) from both data and tasks.
Complete Scene Reconstruction by Merging Images and Laser Scans
Experimental evaluations on two ancient Chinese architecture datasets demonstrate the effectiveness of our proposed complete scene reconstruction pipeline.
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition
On the one hand, we constrain the new parameters not to deviate too far from the original parameters and punish the new system when forgetting original knowledge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Visual Localization Using Sparse Semantic 3D Map
Accurate and robust visual localization under a wide range of viewing condition variations including season and illumination changes, as well as weather and day-night variations, is the key component for many computer vision and robotics applications.
Consistent Dialogue Generation with Self-supervised Feature Learning
Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents.
Understanding the Mechanism of Deep Learning Framework for Lesion Detection in Pathological Images with Breast Cancer
Most of the characteristics learned by the deep learning models have summarized the detection rules that can be recognized by the experienced pathologists, whereas there are still some features may not be intuitive to domain experts but discriminative in classification for machines.
Jointly Optimizing Diversity and Relevance in Neural Response Generation
In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.
Ranked #1 on
Dialogue Generation
on Reddit (multi-ref)
Dialog System Technology Challenge 7
This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems.
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
In this paper, we represent skeletons naturally on graphs, and propose a graph regression based GCN (GR-GCN) for skeleton-based action recognition, aiming to capture the spatio-temporal variation in the data.
Ranked #2 on
Skeleton Based Action Recognition
on Florence 3D
Large Scale Urban Scene Modeling from MVS Meshes
There are two major steps in our framework: segmentation and building modeling.
LDSO: Direct Sparse Odometry with Loop Closure
In this paper we present an extension of Direct Sparse Odometry (DSO) to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO).
Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate
In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 norms and outputs a global feature vector with a global average pooling layer.
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
Face Analysis Project on MXNet
Ranked #5 on
Lightweight Face Recognition
on AgeDB-30
CSfM: Community-based Structure from Motion
The global manner has the advantage of simultaneously estimating all camera poses, but it is usually sensitive to epipolar geometry outliers.
Deep reinforcement learning for time series: playing idealized trading games
Deep Q-learning is investigated as an end-to-end solution to estimate the optimal strategies for acting on time series input.
HSfM: Hybrid Structure-from-Motion
In this work, we propose a new hybrid SfM method to tackle the issues of efficiency, accuracy and robustness in a unified framework.
Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect
Monocular visual odometry (VO) and simultaneous localization and mapping (SLAM) have seen tremendous improvements in accuracy, robustness and efficiency, and have gained increasing popularity over recent years.
Monocular Visual Odometry
Simultaneous Localization and Mapping
Randomized Primal-Dual Proximal Block Coordinate Updates
Assuming mere convexity, we establish its $O(1/t)$ convergence rate in terms of the objective value and feasibility measure.
