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Found 346 papers, 92 papers with code
Building Goal-oriented Document-grounded Dialogue Systems
In this paper, we describe our systems for solving the two Doc2Dial shared task: knowledge identification and response generation.
SemEval-2022 Task 8: Multilingual news article similarity
Thousands of new news articles appear daily in outlets in different languages.
A Three-step Method for Multi-Hop Inference Explanation Regeneration
Then, a selection module is employed to choose those most relative facts in an autoregressive manner, giving a preliminary order for the retrieved facts.
Domain-specific knowledge distillation yields smaller and better models for conversational commerce
We demonstrate that knowledge distillation can be used not only to reduce model size, but to simultaneously adapt a contextual language model to a specific domain.
IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination
This paper aims to recover object materials from posed images captured under an unknown static lighting condition.
Efficient and accurate neural field reconstruction using resistive memory
The GE harnesses the intrinsic stochasticity of resistive memory for efficient input encoding, while the PE achieves precise weight mapping through a Hardware-Aware Quantization (HAQ) circuit.
Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home.
Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
Demonstrating equivalent generative quality to the software baseline, our system achieved remarkable enhancements in generative speed for both unconditional and conditional generation tasks, by factors of 64. 8 and 156. 5, respectively.
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models
In this work, we aim to enhance ensemble diversity by reducing attack transferability.
FlashFace: Human Image Personalization with High-fidelity Identity Preservation
This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt.
Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment
This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way.
Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation
Source-free unsupervised domain adaptation (SFUDA) aims to enable the utilization of a pre-trained source model in an unlabeled target domain without access to source data.
Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
Through a comprehensive evaluation of 24 models across 11 benchmarks, we highlight several potential drawbacks of MCQA, for instance, the inconsistency between the MCQA evaluation and the generation of open-ended responses in practical scenarios.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
In this report, we present the latest model of the Gemini family, Gemini 1. 5 Pro, a highly compute-efficient multimodal mixture-of-experts model capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio.
Ranked #19 on
Code Generation
on HumanEval
Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs
Deep learning methods for perception are the cornerstone of many robotic systems.
PRSA: Prompt Reverse Stealing Attacks against Large Language Models
In the mutation phase, we propose a prompt attention algorithm based on differential feedback to capture these critical features for effectively inferring the target prompts.
InstructEdit: Instruction-based Knowledge Editing for Large Language Models
Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance.
Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?
Parameter-efficient fine-tuning stands as the standard for efficiently fine-tuning large language and vision models on downstream tasks.
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023.
Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise
We investigate both the theoretical and algorithmic aspects of likelihood-based methods for recovering a complex-valued signal from multiple sets of measurements, referred to as looks, affected by speckle (multiplicative) noise.
Deep learning for the design of non-Hermitian topolectrical circuits
Non-Hermitian topological phases can produce some remarkable properties, compared with their Hermitian counterpart, such as the breakdown of conventional bulk-boundary correspondence and the non-Hermitian topological edge mode.
Understanding the Training Speedup from Sampling with Approximate Losses
In this work, we focus on the greedy approach of selecting samples with large \textit{approximate losses} instead of exact losses in order to reduce the selection overhead.
Prompt Learning on Temporal Interaction Graphs
In detail, we propose a temporal prompt generator to offer temporally-aware prompts for different tasks.
Multi-Scale Semantic Segmentation with Modified MBConv Blocks
Recently, MBConv blocks, initially designed for efficiency in resource-limited settings and later adapted for cutting-edge image classification performances, have demonstrated significant potential in image classification tasks.
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models
This paper introduces FedPFT (Federated Learning with Parametric Feature Transfer), a methodology that harnesses the transferability of foundation models to enhance both accuracy and communication efficiency in one-shot FL.
DFML: Decentralized Federated Mutual Learning
In the realm of real-world devices, centralized servers in Federated Learning (FL) present challenges including communication bottlenecks and susceptibility to a single point of failure.
EK-Net:Real-time Scene Text Detection with Expand Kernel Distance
Recently, scene text detection has received significant attention due to its wide application.
Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA
This study focused on evaluating and enhancing the clinical capabilities of LLMs in specific domains, using osteoarthritis (OA) management as a case study.
The Distributional Reward Critic Architecture for Perturbed-Reward Reinforcement Learning
We study reinforcement learning in the presence of an unknown reward perturbation.
Enhancing Blood Flow Assessment in Diffuse Correlation Spectroscopy: A Transfer Learning Approach with Noise Robustness Analysis
While machine learning has demonstrated significant potential for measuring blood flow index (BFi), an open question concerning the success of this approach pertains to its robustness in scenarios involving deviations between datasets with varying Signal-to-Noise Ratios (SNRs) originating from diverse clinical applications and various setups.
Fast Cerebral Blood Flow Analysis via Extreme Learning Machine
We introduce a rapid and precise analytical approach for analyzing cerebral blood flow (CBF) using Diffuse Correlation Spectroscopy (DCS) with the application of the Extreme Learning Machine (ELM).
Transfer the linguistic representations from TTS to accent conversion with non-parallel data
This paper introduces a novel non-autoregressive framework for accent conversion that learns accent-agnostic linguistic representations and employs them to convert the accent in the source speech.
Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition
With the increasing availability of consumer depth sensors, 3D face recognition (FR) has attracted more and more attention.
LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models
Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks, significantly improving task-specific performance.
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation
A simple neural network then learns the implicit mapping from the intra- and inter-sample relations to an adaptive, sample-wise knowledge fusion ratio in a bilevel-optimization manner.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning
In this paper, we evaluate the reasoning capabilities of VLMs along various axes through the lens of geometry problems.
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space
To improve scalability for larger models, one common Bayesian approach is to approximate the global predictive posterior by multiplying local predictive posteriors.
Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training
Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware.
Audio Deepfake Detection with Self-Supervised WavLM and Multi-Fusion Attentive Classifier
In this paper, we report our efforts to combine the self-supervised WavLM model and Multi-Fusion Attentive classifier for audio deepfake detection.
TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation
What we possess are numerous isolated filed-specific datasets, thus, it is appealing to jointly train models across the aggregation of datasets to enhance data volume and diversity.
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models
The VRM reduces the estimation error in ERM by replacing the point-wise kernel estimates with a more precise estimation of true data distribution that reduces the gap between data points \textbf{within each domain}.
Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment
While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment.
LivePhoto: Real Image Animation with Text-guided Motion Control
In particular, considering the facts that (1) text can only describe motions roughly (e. g., regardless of the moving speed) and (2) text may include both content and motion descriptions, we introduce a motion intensity estimation module as well as a text re-weighting module to reduce the ambiguity of text-to-motion mapping.
A Lightweight Clustering Framework for Unsupervised Semantic Segmentation
We thus propose a lightweight clustering framework for unsupervised semantic segmentation.
Utility Fairness in Contextual Dynamic Pricing with Demand Learning
This paper introduces a novel contextual bandit algorithm for personalized pricing under utility fairness constraints in scenarios with uncertain demand, achieving an optimal regret upper bound.
Leverage Staking with Liquid Staking Derivatives (LSDs): Opportunities and Risks
Lido, the leading Liquid Staking Derivative (LSD) provider on Ethereum, allows users to stake an arbitrary amount of ETH to receive stETH, which can be integrated with Decentralized Finance (DeFi) protocols such as Aave.
Learning Disentangled Identifiers for Action-Customized Text-to-Image Generation
Experimental results show that existing subject-driven customization methods fail to learn the representative characteristics of actions and struggle in decoupling actions from context features, including appearance.
YUAN 2.0: A Large Language Model with Localized Filtering-based Attention
In this work, we develop and release Yuan 2. 0, a series of large language models with parameters ranging from 2. 1 billion to 102. 6 billion.
Pruning random resistive memory for optimizing analogue AI
Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network.
Evolutionary City: Towards a Flexible, Agile and Symbiotic System
This paper introduces a novel approach, aiming to enable cities to evolve and respond more effectively to such dynamic demand.
Map-assisted TDOA Localization Enhancement Based On CNN
For signal processing related to localization technologies, non line of sight (NLOS) multipaths have a significant impact on the localization error level.
GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models
In this work, we propose Goal-conditioned Offline Planning (GOPlan), a novel model-based framework that contains two key phases: (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies.
Triplet Attention Transformer for Spatiotemporal Predictive Learning
Spatiotemporal predictive learning offers a self-supervised learning paradigm that enables models to learn both spatial and temporal patterns by predicting future sequences based on historical sequences.
PaLI-3 Vision Language Models: Smaller, Faster, Stronger
This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger.
Ranked #2 on
Temporal/Casual QA
on NExT-QA
(using extra training data)
Can We Edit Multimodal Large Language Models?
In this paper, we focus on editing Multimodal Large Language Models (MLLMs).
Uni3DETR: Unified 3D Detection Transformer
In this paper, we propose Uni3DETR, a unified 3D detector that addresses indoor and outdoor 3D detection within the same framework.
Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning
In this paper, we develop an online robust policy evaluation procedure, and establish the limiting distribution of our estimator, based on its Bahadur representation.
Unveiling the Pitfalls of Knowledge Editing for Large Language Models
This paper pioneers the investigation into the potential pitfalls associated with knowledge editing for LLMs.
Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning
The DRL policy aims to optimize trading fees earned by LPs against associated costs, such as gas fees and hedging expenses, which is referred to as loss-versus-rebalancing (LVR).
OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation
When integrated with powerful 2D open-world models such as ODISE and GroundingDINO, excellent results were observed on open-vocabulary instance segmentation.
3D Open-Vocabulary Instance Segmentation
3D Open-Vocabulary Object Detection
+5
SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding
Simultaneously, resource consumption of a single-GPU can be diminished by up to 69%, thus enabling the multiple GPU-based training and acceleration encompassing millions of nodes and billions of edges.
Understanding Hessian Alignment for Domain Generalization
We validate the OOD generalization ability of proposed methods in different scenarios, including transferability, severe correlation shift, label shift and diversity shift.
SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation
To address the conflicts among multiple tasks and meet the different demands of tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis subnet, which can adaptively select the features from optimal levels and channels to perform the desired tasks.
Understanding the Role of Layer Normalization in Label-Skewed Federated Learning
In this work, we reveal the profound connection between layer normalization and the label shift problem in federated learning.
Teacher-Student Architecture for Knowledge Distillation: A Survey
Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement.
Convolutional Occupancy Models for Dense Packing of Complex, Novel Objects
Dense packing in pick-and-place systems is an important feature in many warehouse and logistics applications.
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
AnyDoor: Zero-shot Object-level Image Customization
This work presents AnyDoor, a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations in a harmonious way.
Is ChatGPT a Good Personality Recognizer? A Preliminary Study
Concretely, we employ a variety of prompting strategies to explore ChatGPT's ability in recognizing personality from given text, especially the level-oriented prompting strategy we designed for guiding ChatGPT in analyzing given text at a specified level.
Active Learning in Physics: From 101, to Progress, and Perspective
Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence.
Memory-Query Tradeoffs for Randomized Convex Optimization
We show that any randomized first-order algorithm which minimizes a $d$-dimensional, $1$-Lipschitz convex function over the unit ball must either use $\Omega(d^{2-\delta})$ bits of memory or make $\Omega(d^{1+\delta/6-o(1)})$ queries, for any constant $\delta\in (0, 1)$ and when the precision $\epsilon$ is quasipolynomially small in $d$.
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models
Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models.
2D-Shapley: A Framework for Fragmented Data Valuation
Data valuation -- quantifying the contribution of individual data sources to certain predictive behaviors of a model -- is of great importance to enhancing the transparency of machine learning and designing incentive systems for data sharing.
Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal
Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification.
PolyVoice: Language Models for Speech to Speech Translation
For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model.
PaLI-X: On Scaling up a Multilingual Vision and Language Model
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture.
Ranked #1 on
Fine-Grained Image Recognition
on OVEN
Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality
Stochastic gradient descent with momentum (SGDM) has been widely used in many machine learning and statistical applications.
CONA: A novel CONtext-Aware instruction paradigm for communication using large language model
We introduce CONA, a novel context-aware instruction paradigm for effective knowledge dissemination using generative pre-trained transformer (GPT) models.
Text Generation with Speech Synthesis for ASR Data Augmentation
In this work, we explore text augmentation for ASR using large-scale pre-trained neural networks, and systematically compare those to traditional text augmentation methods.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Continual Multimodal Knowledge Graph Construction
Multimodal Knowledge Graph Construction (MKGC) involves creating structured representations of entities and relations using multiple modalities, such as text and images.
Schema-adaptable Knowledge Graph Construction
This necessitates a system that can handle evolving schema automatically to extract information for KGC.
Self-Supervised Instance Segmentation by Grasping
Leveraging this insight, we learn a grasp segmentation model to segment the grasped object from before and after grasp images.
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN
Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous scenes, and as such can cause critical errors in high-performance applications.
Transformer-based models and hardware acceleration analysis in autonomous driving: A survey
The paper also highlights the challenges, trends, and current insights in Transformer-based models, addressing their hardware deployment and acceleration issues within the context of long-term autonomous driving applications.
EEGSN: Towards Efficient Low-latency Decoding of EEG with Graph Spiking Neural Networks
A vast majority of spiking neural networks (SNNs) are trained based on inductive biases that are not necessarily a good fit for several critical tasks that require low-latency and power efficiency.
The Future of ChatGPT-enabled Labor Market: A Preliminary Study in China
To this end, in this paper, we introduce a preliminary data-driven study on the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation.
Head-tail Loss: A simple function for Oriented Object Detection and Anchor-free models
However, for the second point, the minimum distance between the prediction and either the head or tail of the groundtruth is used.
FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead
We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI).
CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis
Diffusion model has emerged as an effective technique for image synthesis by modelling complex and variable data distributions.
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution
Moreover, DisC-Diff leverages a disentangled multi-stream network to fully exploit complementary information from multi-contrast MRI, improving model interpretation under multiple conditions of multi-contrast inputs.
Detecting Everything in the Open World: Towards Universal Object Detection
In this paper, we formally address universal object detection, which aims to detect every scene and predict every category.
Open-vocabulary Panoptic Segmentation with Embedding Modulation
Open-vocabulary image segmentation is attracting increasing attention due to its critical applications in the real world.
ScribbleSeg: Scribble-based Interactive Image Segmentation
We hope this could serve as a more powerful and general solution for interactive segmentation.
Knowledge-augmented Few-shot Visual Relation Detection
Inspired by this, we devise a knowledge-augmented, few-shot VRD framework leveraging both textual knowledge and visual relation knowledge to improve the generalization ability of few-shot VRD.
Statistical Inference with Stochastic Gradient Methods under $φ$-mixing Data
In this paper, we propose a mini-batch SGD estimator for statistical inference when the data is $\phi$-mixing.
Uniformity Testing over Hypergrids with Subcube Conditioning
We give an algorithm for testing uniformity of distributions supported on hypergrids $[m_1] \times \cdots \times [m_n]$, which makes $\smash{\widetilde{O}(\text{poly}(m)\sqrt{n}/\epsilon^2)}$ many queries to a subcube conditional sampling oracle with $m=\max_i m_i$.
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering.
The most probable dynamics of receptor-ligand binding on cell membrane
We consider the dynamics of a receptor binding to a ligand on the cell membrane, where the receptor and ligand perform different motions and are thus modeled by stochastic differential equations with Gaussian noise or non-Gaussian noise.
3D Molecular Generation via Virtual Dynamics
Extensive experiment results on pocket-based molecular generation demonstrate that VD-Gen can generate novel 3D molecules to fill the target pocket cavity with high binding affinities, significantly outperforming previous baselines.
Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Assortment optimization has received active explorations in the past few decades due to its practical importance.
A Framework of Transaction Packaging in High-throughput Blockchains
We develop a model of coordination and allocation of decentralized multi-sided markets, in which our theoretical analysis is promisingly optimizing the decentralized transaction packaging process at high-throughput blockchains or Web 3. 0 platforms.
Editing Language Model-based Knowledge Graph Embeddings
To address this issue, we propose a new task of editing language model-based KG embeddings in this paper.
Dynamically Instance-Guided Adaptation: A Backward-Free Approach for Test-Time Domain Adaptive Semantic Segmentation
In this paper, we study the application of Test-time domain adaptation in semantic segmentation (TTDA-Seg) where both efficiency and effectiveness are crucial.
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent
With the fast development of big data, it has been easier than before to learn the optimal decision rule by updating the decision rule recursively and making online decisions.
Hyperspherical Quantization: Toward Smaller and More Accurate Models
Model quantization enables the deployment of deep neural networks under resource-constrained devices.
FreeEnricher: Enriching Face Landmarks without Additional Cost
To evaluate our method, we manually label the dense landmarks on 300W testset.
Ranked #1 on
Face Alignment
on 300W
Interpretable Node Representation with Attribute Decoding
We further propose a new learning model, interpretable NOde Representation with Attribute Decoding (NORAD).
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization
Recent research in robust optimization has shown an overfitting-like phenomenon in which models trained against adversarial attacks exhibit higher robustness on the training set compared to the test set.
Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge
Extensive experiments demonstrate that PICSO can dramatically outperform the original PLMs and the other knowledge and synonym injection models on four different similarity-oriented tasks.
NeurIPS 2022 Competition: Driving SMARTS
The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combination of naturalistic AD data and open-source simulation platform SMARTS.
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction
Open Relation Extraction (OpenRE) aims to discover novel relations from open domains.
Self-Attentive Sequential Recommendation with Cheap Causal Convolutions
We evaluate the effectiveness of the proposed model in terms of both accurate and calibrated sequential recommendation.
Teacher-Student Architecture for Knowledge Learning: A Survey
To tackle this issue, Teacher-Student architectures were first utilized in knowledge distillation, where simple student networks can achieve comparable performance to deep teacher networks.
Dissecting Deep Metric Learning Losses for Image-Text Retrieval
In the event that the gradients are not integrable to a valid loss function, we implement our proposed objectives such that they would directly operate in the gradient space instead of on the losses in the embedding space.
Ranked #8 on
Cross-Modal Retrieval
on Flickr30k
(using extra training data)
Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction
With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performance.
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
This paper presents an empirical study to build relation extraction systems in low-resource settings.
Distributed Estimation and Inference for Semi-parametric Binary Response Models
The development of modern technology has enabled data collection of unprecedented size, which poses new challenges to many statistical estimation and inference problems.
WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments
It provides realistic 3D environments of variable complexity, various tasks, and multiple modes of interaction, where agents can learn to perceive 3D environments, navigate and plan, compete and cooperate in a human-like manner.
Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction
3D bounding boxes are a widespread intermediate representation in many computer vision applications.
MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text
While language Models store a massive amount of world knowledge implicitly in their parameters, even very large models often fail to encode information about rare entities and events, while incurring huge computational costs.
PaLI: A Jointly-Scaled Multilingual Language-Image Model
PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages.
PreSTU: Pre-Training for Scene-Text Understanding
The ability to recognize and reason about text embedded in visual inputs is often lacking in vision-and-language (V&L) models, perhaps because V&L pre-training methods have often failed to include such an ability in their training objective.
MaXM: Towards Multilingual Visual Question Answering
In this paper, we propose scalable solutions to multilingual visual question answering (mVQA), on both data and modeling fronts.
Deep Baseline Network for Time Series Modeling and Anomaly Detection
By using this deep network, we can easily locate the baseline position and then provide reliable and interpretable anomaly detection result.
Majority Vote for Distributed Differentially Private Sign Selection
Simulation studies are conducted to demonstrate the effectiveness of our proposed method.
Two-stage Hypothesis Tests for Variable Interactions with FDR Control
In many scenarios such as genome-wide association studies where dependences between variables commonly exist, it is often of interest to infer the interaction effects in the model.
DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders
Similar to other differentially private (DP) learners, the major challenge for DPGM is also how to achieve a subtle balance between utility and privacy.
Delta Hedging Liquidity Positions on Automated Market Makers
The dominant measure of loss in a Liquidity Position is Impermanent Loss.
Gradient-based Bi-level Optimization for Deep Learning: A Survey
In this survey, we first give a formal definition of the gradient-based bi-level optimization.
Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancing
We introduce the regularized revenue, i. e., the total revenue with a balancing regularization, as our objective to incorporate fair resource-consumption balancing into the revenue maximization goal.
Robust One Round Federated Learning with Predictive Space Bayesian Inference
In contrast, our method performs the aggregation on the predictive posteriors, which are typically easier to approximate owing to the low-dimensionality of the output space.
Mitigating Data Heterogeneity in Federated Learning with Data Augmentation
Federated Learning (FL) is a prominent framework that enables training a centralized model while securing user privacy by fusing local, decentralized models.
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
We present Federated Bayesian Neural Regression (FedBNR), an algorithm that learns a scalable stand-alone global federated GP that respects clients' privacy.
ReCo: A Dataset for Residential Community Layout Planning
In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date.
Unbiased Implicit Feedback via Bi-level Optimization
Recent works reveal a huge gap between the implicit feedback and user-item relevance due to the fact that implicit feedback is also closely related to the item exposure.
Neural 3D Reconstruction in the Wild
We are witnessing an explosion of neural implicit representations in computer vision and graphics.
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset
Research in massively multilingual image captioning has been severely hampered by a lack of high-quality evaluation datasets.
All You May Need for VQA are Image Captions
Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation.
Memory Bounds for Continual Learning
We make novel uses of communication complexity to establish that any continual learner, even an improper one, needs memory that grows linearly with $k$, strongly suggesting that the problem is intractable.
FocalClick: Towards Practical Interactive Image Segmentation
To make the model work with preexisting masks, we formulate a sub-task termed Interactive Mask Correction, and propose Progressive Merge as the solution.
Ranked #1 on
Interactive Segmentation
on GrabCut
(using extra training data)
Optimization of Directional Landmark Deployment for Visual Observer on SE(3)
An optimization method is proposed in this paper for novel deployment of given number of directional landmarks (location and pose) within a given region in the 3-D task space.
Multi-modal learning for predicting the genotype of glioma
In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks.
Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey
However, the temporal information embedded in the dynamic graphs brings new challenges in analyzing and deploying them.
Coverage Optimization of Camera Network for Continuous Deformable Object
To reduce the computational complexity, some feature points are carefully selected representing the continuous deformation process, and the visual coverage for the deformable object is transferred to cover the specific feature points.
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks
With the increasing abundance of pretrained models in recent years, the problem of selecting the best pretrained checkpoint for a particular downstream classification task has been gaining increased attention.
Ranked #5 on
Transferability
on classification benchmark
AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine
Accurately identifying patient's status through medical images plays an important role in diagnosis and treatment.
On Digital Subcarrier Multiplexing under A Bandwidth Limitation and ASE Noise
We show that digital subcarrier multiplexing (DSM) systems require much greater complexity for Nyquist pulse shaping than single-carrier (SC) systems, and it is a misconception that both systems use the same bandwidth when using the same pulse shaping.
Proportional Fairness in Federated Learning
With the increasingly broad deployment of federated learning (FL) systems in the real world, it is critical but challenging to ensure fairness in FL, i. e. reasonably satisfactory performances for each of the numerous diverse clients.
Ontology-enhanced Prompt-tuning for Few-shot Learning
Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text.
S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement
In S2MS framework, both the input and output labels are noisy images.
Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma
Further, the collaborative learning model achieves better performance than either the CNN or the GNN alone.
Generalized DataWeighting via Class-Level Gradient Manipulation
To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.
Leveraging Intrinsic Gradient Information for Further Training of Differentiable Machine Learning Models
Designing models that produce accurate predictions is the fundamental objective of machine learning (ML).
Interval-valued q-Rung Orthopair Fuzzy Choquet Integral Operators and Its Application in Group Decision Making
It is more flexible for decision makers to evaluate by interval-valued q-rung orthopair fuzzy set (IVq-ROFS), which offers fuzzy decision-making more applicational space.
Fairness-aware Online Price Discrimination with Nonparametric Demand Models
To handle this general class, we propose a soft fairness constraint and develop a dynamic pricing policy that achieves $\tilde{O}(T^{4/5})$ regret.
On the Complexity of Dynamic Submodular Maximization
We study dynamic algorithms for the problem of maximizing a monotone submodular function over a stream of $n$ insertions and deletions.
Generalized Data Weighting via Class-level Gradient Manipulation
To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.
Brain-inspired feature exaggeration in generative replay for continual learning
The catastrophic forgetting of previously learnt classes is one of the main obstacles to the successful development of a reliable and accurate generative continual learning model.
Active Learning for Contextual Search with Binary Feedbacks
In particular, for a sequence of arriving context vectors, with each context associated with an underlying value, the decision-maker either makes a query at a certain point or skips the context.
AutoMO-Mixer: An automated multi-objective multi-layer perspecton Mixer model for medical image based diagnosis
The experimental study demonstrated that AutoMO-Mixer can obtain better performance compared with Mixer and CNN.
MOBA: Multi-teacher Model Based Reinforcement Learning
Results show that, compared with SOTA model-free methods, our method can improve the data efficiency and system performance by up to 75% and 10%, respectively.
PAC-Bayes Information Bottleneck
Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.
Deep Learning based Model-free Robust Load Restoration to Enhance Bulk System Resilience with Wind Power Penetration
This paper proposes a new deep learning (DL) based model-free robust method for bulk system on-line load restoration with high penetration of wind power.
Differential Privacy in Personalized Pricing with Nonparametric Demand Models
In the recent decades, the advance of information technology and abundant personal data facilitate the application of algorithmic personalized pricing.
Risk-Adjusted Valuation for Real Option Decisions
We propose a risk-adjusted valuation model to facilitate investors' subjective decision making, in response to the market valuation of an investment opportunity.
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning
X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings for each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings.
Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks
Here we propose a method to predict IDH mutation using GNN, based on the structural brain network of patients.
Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma
In this paper, we propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction.
CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse
Accordingly, we propose CONNER, a cascade count and measurement extraction tool that can identify entities and the corresponding relations in a two-step pipeline model.
Pruning Ternary Quantization
To simultaneously optimize bit-width, model size, and accuracy, we propose pruning ternary quantization (PTQ): a simple, effective, symmetric ternary quantization method.
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction
However, these methods lack transparency when compared to simpler baselines, and as a result their robustness against adversarial attacks is a possible point of concern: could one or a few small adversarial modifications to the network have a large impact on the link prediction performance when using a network embedding model?
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction.
PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System
Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i. e., mappings) between two KGs.
Field Embedding: A Unified Grain-Based Framework for Word Representation
Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings.
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
Despite recent advances in its theoretical understanding, there still remains a significant gap in the ability of existing PAC-Bayesian theories on meta-learning to explain performance improvements in the few-shot learning setting, where the number of training examples in the target tasks is severely limited.
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities.
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding
Knowledge Graph (KG) alignment is to discover the mappings (i. e., equivalent entities, relations, and others) between two KGs.
Accent Recognition with Hybrid Phonetic Features
The performance of voice-controlled systems is usually influenced by accented speech.
Interventional Aspect-Based Sentiment Analysis
Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when encountering confounder such as non-target aspects.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management
For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL. To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs.
FEW-SHOTLEARNING WITH WEAK SUPERVISION
Few-shot meta-learning methods aim to learn the common structure shared across a set of tasks to facilitate learning new tasks with small amounts of data.
BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification
The proposed BrainNetGAN is a generative adversarial network variant to augment the brain structural connectivity matrices for binary dementia classification tasks.
No Weighted-Regret Learning in Adversarial Bandits with Delays
Using these bounds, we show that FKM and EXP3 have no weighted-regret even for $d_{t}=O\left(t\log t\right)$.
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms
Our results show that the proposed method can successfully adapt a trained policy to different robotic platforms with novel physical parameters and the superiority of our meta-learning algorithm compared to state-of-the-art methods for the introduced few-shot policy adaptation problem.
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference
Based on the proposed VRMOM estimator, we develop a general distributed inference algorithm that is robust against Byzantine failures.
Lifelong Learning based Disease Diagnosis on Clinical Notes
Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i. e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks.
Learning a Product Relevance Model from Click-Through Data in E-Commerce
We propose a novel way to consider samples of different relevance confidence, and come up with a new training objective to learn a robust relevance model with desirable score distribution.
Does Probabilistic Constellation Shaping Benefit IM-DD Systems without Optical Amplifiers?
Probabilistic constellation shaping (PCS) has been widely applied to amplified coherent optical transmissions owing to its shaping gain over the uniform signaling and fine-grained rate adaptation to the underlying fiber channel condition.
Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods
We first present the asymptotic distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW) estimators, whose asymptotic covariance matrices depend on the distribution of search directions and the function-value query complexity.
Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the State of Qatar
During the current COVID-19 pandemic, decision makers are tasked with implementing and evaluating strategies for both treatment and disease prevention.
Methodology Applications
Experimental implementation of precisely tailored light-matter interaction via inverse engineering
Here, we implement fast and robust control for the state preparation and state engineering in a rare-earth ions system.
Quantum Physics
Enquire One's Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion
Taxonomy is a hierarchically structured knowledge graph that plays a crucial role in machine intelligence.
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data
A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the low SNR time-domain data (24 SA) to the high SNR one (128 SA).
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma
EMReDL showed to effectively segment the infiltrated tumor from the partially labelled region of potential infiltration.
Adversarial Combinatorial Bandits with General Non-linear Reward Functions
{The adversarial combinatorial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback.}
Conditional Diffusion for Interactive Segmentation
In click-based interactive segmentation, the mask extraction process is dictated by positive/negative user clicks; however, most existing methods do not fully exploit the user cues, requiring excessive numbers of clicks for satisfactory results.
