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Found 116 papers, 30 papers with code
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
Nevertheless, how to efficiently model the spatial-temporal skeleton graph without introducing extra computation burden is a challenging problem for industrial deployment.
Orchestrate Multimodal Data with Batch Post-Balancing to Accelerate Multimodal Large Language Model Training
Multimodal large language models (MLLMs), such as GPT-4o, are garnering significant attention.
DEPT: Deep Extreme Point Tracing for Ultrasound Image Segmentation
The performance of our method approaches that of the fully supervised method and outperforms several existing weakly supervised methods.
CLAD: Constrained Latent Action Diffusion for Vision-Language Procedure Planning
By evaluating ablated versions of our method, we further show that the proposed integration of the action and observation representations learnt in the VAE latent space is key to these performance improvements.
A Synergy Scoring Filter for Unsupervised Anomaly Detection with Noisy Data
To achieve this, we introduce the Synergy Scoring Filter (SSFilter), the first fully unsupervised anomaly detection approach to leverage sample-level filtering.
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies, while Graph Transformers (GTs) are considered superior due to their global attention mechanisms.
Ranked #1 on
Graph Property Prediction
on ogbg-ppa
EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents
To address this, we propose EmbodiedEval, a comprehensive and interactive evaluation benchmark for MLLMs with embodied tasks.
TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation
The integration of AI in education offers significant potential to enhance learning efficiency.
SmartEraser: Remove Anything from Images using Masked-Region Guidance
This paradigm retains the masked region in the input, using it as guidance for the removal process.
Radiology Report Generation via Multi-objective Preference Optimization
Next, the RRG model is optimized to align with the preference vector by optimizing such a reward via RL.
eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules
Link prediction (LP) is crucial for Knowledge Graphs (KG) completion but commonly suffers from interpretability issues.
Long-time Integration of Nonlinear Wave Equations with Neural Operators
In this paper, we focus on solving the long-time integration of nonlinear wave equations via neural operators by replacing the initial condition with the prediction in a recurrent manner.
Diffusion-based Semi-supervised Spectral Algorithm for Regression on Manifolds
We introduce a novel diffusion-based spectral algorithm to tackle regression analysis on high-dimensional data, particularly data embedded within lower-dimensional manifolds.
Distributed Learning with Discretely Observed Functional Data
This demonstrates that the proposed regularity conditions are reasonable and that the convergence analysis under these conditions is tight, capturing the essential characteristics of functional linear regression.
Truncated Kernel Stochastic Gradient Descent on Spheres
Inspired by the structure of spherical harmonics, we propose the truncated kernel stochastic gradient descent (T-kernel SGD) algorithm with a least-square loss function for spherical data fitting.
Large Language Models Overcome the Machine Penalty When Acting Fairly but Not When Acting Selfishly or Altruistically
We design three types of LLMs: (i) Cooperative, aiming to assist its human associate; (ii) Selfish, focusing solely on maximizing its self-interest; and (iii) Fair, balancing its own and collective interest, while slightly prioritizing self-interest.
OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction
Accurate traffic forecasting is crucial for effective urban planning and transportation management, enabling efficient resource allocation and enhanced travel experiences.
LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations
The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality.
Stochastic Gradient Descent for Two-layer Neural Networks
This paper presents a comprehensive study on the convergence rates of the stochastic gradient descent (SGD) algorithm when applied to overparameterized two-layer neural networks.
Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions
We propose MToMnet - a Theory of Mind (ToM) neural network for predicting beliefs and their dynamics during human social interactions from multimodal input.
MFE-ETP: A Comprehensive Evaluation Benchmark for Multi-modal Foundation Models on Embodied Task Planning
To this end, based on the characteristics of embodied task planning, we first develop a systematic evaluation framework, which encapsulates four crucial capabilities of MFMs: object understanding, spatio-temporal perception, task understanding, and embodied reasoning.
Multi-Modal Video Dialog State Tracking in the Wild
Then, it predicts the missing underlying structure of the selected constituents of each modality by learning local latent graphs using a novel multi-modal graph structure learning method.
Benchmarking Mental State Representations in Language Models
We are the first to study how prompt variations impact probing performance on theory of mind tasks.
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressiveness and impressive performance reported on standard node classification benchmarks, often significantly outperforming GNNs.
Ranked #1 on
Node Classification
on questions
A novel robust meta-analysis model using the $t$ distribution for outlier accommodation and detection
Although robust modeling using the $t$ distribution is an appealing idea, the existing work, that explores the use of the $t$ distribution only for random effects, involves complicated numerical integration and numerical optimization.
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning
Ridgeless regression has garnered attention among researchers, particularly in light of the ``Benign Overfitting'' phenomenon, where models interpolating noisy samples demonstrate robust generalization.
Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identifiers (node IDs), to tackle inference challenges on large-scale graphs.
Limits of Theory of Mind Modelling in Dialogue-Based Collaborative Plan Acquisition
This finding calls for a deeper understanding of the role of ToM in CPA and beyond, as well as new methods for modelling and evaluating mental states in computational collaborative agents.
MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection
In our methodology, any dataset can be unified under the framework of feature-rich anomaly detection, in a way that the benefits far outweigh the drawbacks.
Ranked #1 on
Anomaly Classification
on GoodsAD
(AUROC metric)
VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images
Our method is based on the Semantic Pointer Architecture (SPA) to encode objects in a hyperdimensional vector space.
LEGENT: Open Platform for Embodied Agents
Despite advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), their integration into language-grounded, human-like embodied agents remains incomplete, hindering complex real-life task performance in physical environments.
Integrating LSTM and BERT for Long-Sequence Data Analysis in Intelligent Tutoring Systems
However, with the development of Intelligent Tutoring Systems, large-scale datasets containing long-sequence data began to emerge.
Solving Parametric PDEs with Radial Basis Functions and Deep Neural Networks
We propose the POD-DNN, a novel algorithm leveraging deep neural networks (DNNs) along with radial basis functions (RBFs) in the context of the proper orthogonal decomposition (POD) reduced basis method (RBM), aimed at approximating the parametric mapping of parametric partial differential equations on irregular domains.
An Optimization Framework to Personalize Passive Cardiac Mechanics
The iFEA framework relies on a novel nested optimization scheme, in which the outer iterations utilize a traditional optimization method to best approximate material parameters that fit image data, while the inner iterations employ an augmented Sellier's algorithm to estimate the stress-free reference configuration.
ActionDiffusion: An Action-aware Diffusion Model for Procedure Planning in Instructional Videos
We show that by adding action embeddings into the noise mask the diffusion model can better learn action temporal dependencies and increase the performances on procedure planning.
Spectral Algorithms on Manifolds through Diffusion
Our paper introduces a new perspective, asserting that input data are situated within a low-dimensional manifold embedded in a higher-dimensional Euclidean space.
A Safe Screening Rule with Bi-level Optimization of $ν$ Support Vector Machine
Support vector machine (SVM) has achieved many successes in machine learning, especially for a small sample problem.
Self-Supervised Representation Learning with Meta Comprehensive Regularization
Specifically, we update our proposed model through a bi-level optimization mechanism, enabling it to capture comprehensive features.
HiGPT: Heterogeneous Graph Language Model
However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets.
UrbanGPT: Spatio-Temporal Large Language Models
These findings highlight the potential of building large language models for spatio-temporal learning, particularly in zero-shot scenarios where labeled data is scarce.
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
This study investigates leveraging stochastic gradient descent (SGD) to learn operators between general Hilbert spaces.
Zero-Inflated Bandits
Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency.
Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research
When users perceive AI systems as mindful, independent agents, they hold them responsible instead of the AI experts who created and designed these systems.
RJUA-QA: A Comprehensive QA Dataset for Urology
We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications.
Neural Reasoning About Agents' Goals, Preferences, and Actions
We propose the Intuitive Reasoning Network (IRENE) - a novel neural model for intuitive psychological reasoning about agents' goals, preferences, and actions that can generalise previous experiences to new situations.
Improving Self-supervised Molecular Representation Learning using Persistent Homology
Self-supervised learning (SSL) has great potential for molecular representation learning given the complexity of molecular graphs, the large amounts of unlabelled data available, the considerable cost of obtaining labels experimentally, and the hence often only small training datasets.
A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data
This paper presents a comparative analysis of the COVID-19 infodemic in the English and Chinese languages, utilizing textual data extracted from social media platforms.
$\mathbb{VD}$-$\mathbb{GR}$: Boosting $\mathbb{V}$isual $\mathbb{D}$ialog with Cascaded Spatial-Temporal Multi-Modal $\mathbb{GR}$aphs
We propose $\mathbb{VD}$-$\mathbb{GR}$ - a novel visual dialog model that combines pre-trained language models (LMs) with graph neural networks (GNNs).
GraphGPT: Graph Instruction Tuning for Large Language Models
The open-sourced model implementation of our GraphGPT is available at https://github. com/HKUDS/GraphGPT.
Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels
To enhance kernel flexibility, this paper introduces the concept of Locally-Adaptive-Bandwidths (LAB) as trainable parameters to enhance the Radial Basis Function (RBF) kernel, giving rise to the LAB RBF kernel.
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
Empirically, we demonstrate that graph transformers with HDSE excel in graph classification, regression on 7 graph-level datasets, and node classification on 11 large-scale graphs, including those with up to a billion nodes.
Ranked #5 on
Graph Regression
on ZINC-500k
Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
In this paper, we establish rigorous analysis of the physics-informed convolutional neural network (PICNN) for solving PDEs on the sphere.
Classification with Deep Neural Networks and Logistic Loss
In this paper, we aim to fill this gap by establishing a novel and elegant oracle-type inequality, which enables us to deal with the boundedness restriction of the target function, and using it to derive sharp convergence rates for fully connected ReLU DNN classifiers trained with logistic loss.
OSP: Boosting Distributed Model Training with 2-stage Synchronization
However, these two types of methods can result in accuracy loss due to discarded gradients and have limited enhancement on the throughput of model synchronization, respectively.
Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI
Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications.
STM-UNet: An Efficient U-shaped Architecture Based on Swin Transformer and Multi-scale MLP for Medical Image Segmentation
Deep learning based models for medical image segmentation have made great progress in recent years.
Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs
Experiment outcomes show that the F1 score of best GF profile significantly outperforms alternative methods of impact indicators and bibliometric networks in all the 6 computer science fields considered.
Optimality of Robust Online Learning
In this paper, we study an online learning algorithm with a robust loss function $\mathcal{L}_{\sigma}$ for regression over a reproducing kernel Hilbert space (RKHS).
Pairwise Ranking with Gaussian Kernels
Regularized pairwise ranking with Gaussian kernels is one of the cutting-edge learning algorithms.
Cross-modal information fusion for voice spoofing detection
Researchers have proposed many methods to defend against these attacks, but in the existing methods, researchers just focus on speech features.
Ranked #1 on
Voice Anti-spoofing
on ASVspoof 2019 - PA
Exit options sustain altruistic punishment and decrease the second-order free-riders, but it is not a panacea
Altruistic punishment, where individuals incur personal costs to punish others who have harmed third parties, presents an evolutionary conundrum as it undermines individual fitness.
Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generation
To leverage information from text pairs, we additionally introduce a novel supervised model we call dual directional learning (DDL), which is designed to integrate with our proposed VSAR model.
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression
Previous analysis of regularized functional linear regression in a reproducing kernel Hilbert space (RKHS) typically requires the target function to be contained in this kernel space.
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate
In the proposed family, EquiStatic has a degree of $\Theta(\ln(n))$, where $n$ is the network size, and a series of time-dependent one-peer topologies, EquiDyn, has a constant degree of 1.
Capacity dependent analysis for functional online learning algorithms
This article provides convergence analysis of online stochastic gradient descent algorithms for functional linear models.
INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations
XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, which addresses explainability and transparency.
Coefficient-based Regularized Distribution Regression
Compared with the kernel methods for distribution regression in the literature, the algorithm under consideration does not require the kernel to be symmetric and positive semi-definite and hence provides a simple paradigm for designing indefinite kernel methods, which enriches the theme of the distribution regression.
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training
Retrosynthesis prediction is one of the fundamental challenges in organic chemistry and related fields.
Ranked #17 on
Single-step retrosynthesis
on USPTO-50k
Solving parametric partial differential equations with deep rectified quadratic unit neural networks
In this study, we investigate the expressive power of deep rectified quadratic unit (ReQU) neural networks for approximating the solution maps of parametric PDEs.
Learning with Asymmetric Kernels: Least Squares and Feature Interpretation
Asymmetric kernels naturally exist in real life, e. g., for conditional probability and directed graphs.
Dense Contrastive Visual-Linguistic Pretraining
To overcome these issues, we propose unbiased Dense Contrastive Visual-Linguistic Pretraining (DCVLP), which replaces the region regression and classification with cross-modality region contrastive learning that requires no annotations.
A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data
Diabetic retinopathy (DR) is one of the major blindness-causing diseases currently known.
Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs
We consider the decentralized optimization problem, where a network of $n$ agents aims to collaboratively minimize the average of their individual smooth and convex objective functions through peer-to-peer communication in a directed graph.
Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks
The second algorithm is a broadcast-like version of CPP (B-CPP), and it also achieves linear convergence rate under the same conditions on the objective functions.
Multi-modal Scene-compliant User Intention Estimation in Navigation
A multi-modal framework to generate user intention distributions when operating a mobile vehicle is proposed in this work.
Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums
Learners may post their feelings of confusion and struggle in the respective MOOC forums, but with the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention.
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition
Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered.
Topological charge engineering in lasing bound states in continuum
We experimentally analyze all four observed lasing BICs by imaging their far-field polarization vortices and their associated topological charges.
Optics
Fast algorithms for robust principal component analysis with an upper bound on the rank
The first type of algorithm applies regularization terms on the singular values of a matrix to obtain a low-rank matrix.
Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities
While Massive Open Online Course (MOOCs) platforms provide knowledge in a new and unique way, the very high number of dropouts is a significant drawback.
Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students
Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students.
Social Interactions Clustering MOOC Students: An Exploratory Study
An exploratory study on social interactions of MOOC students in FutureLearn was conducted, to answer "how can we cluster students based on their social interactions?"
Contrastive Visual-Linguistic Pretraining
We evaluate CVLP on several down-stream tasks, including VQA, GQA and NLVR2 to validate the superiority of contrastive learning on multi-modality representation learning.
Leader-Driven Opinion Dynamics in Signed Social Networks With Asynchronous Trust/Distrust Level Evolution
Trust and distrust are common in the opinion interactions among agents in social networks, and they are described by the edges with positive and negative weights in the signed digraph, respectively.
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
Besides, from the data aspect, we introduce a skeletal data decoupling technique to emphasize the specific characteristics of space/time and different motion scales, resulting in a more comprehensive understanding of the human actions. To test the effectiveness of the proposed method, extensive experiments are conducted on four challenging datasets for skeleton-based gesture and action recognition, namely, SHREC, DHG, NTU-60 and NTU-120, where DSTA-Net achieves state-of-the-art performance on all of them.
Ranked #38 on
Skeleton Based Action Recognition
on NTU RGB+D
Analysis of Regularized Least Squares in Reproducing Kernel Krein Spaces
In this paper, we study the asymptotic properties of regularized least squares with indefinite kernels in reproducing kernel Krein spaces (RKKS).
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition
The two perspectives are orthogonal and complementary to each other; and by fusing them in a unified framework, our method achieves a more comprehensive understanding of the skeleton data.
Sub/super-stochastic matrix with applications to bipartite tracking control over signed networks
In the first part, we examine the dynamics of bipartite tracking for first-order MASs, second-order MASs and general linear MASs in the presence of asynchronous interactions, respectively.
Mobility Inference on Long-Tailed Sparse Trajectory
How can we infer such a mobility model from the single trajectory information?
Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering
To solve the issue for the intermediate layers, we propose an efficient Quaternion Block Network (QBN) to learn interaction not only for the last layer but also for all intermediate layers simultaneously.
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
Second, the second-order information of the skeleton data, i. e., the length and orientation of the bones, is rarely investigated, which is naturally more informative and discriminative for the human action recognition.
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision
Existing methods exploit the joint positions to extract the body-part features from the activation map of the convolutional networks to assist human action recognition.
Non-Local Graph Convolutional Networks for Skeleton-Based Action Recognition
However, the topology of the graph is set by hand and fixed over all layers, which may be not optimal for the action recognition task and the hierarchical CNN structures.
Ranked #69 on
Skeleton Based Action Recognition
on NTU RGB+D
Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation
Rapidly estimating the remaining wall thickness (RWT) is paramount for the non-destructive condition assessment evaluation of large critical metallic pipelines.
Skeleton-Based Action Recognition With Directed Graph Neural Networks
The skeleton data have been widely used for the action recognition tasks since they can robustly accommodate dynamic circumstances and complex backgrounds.
Ranked #9 on
Skeleton Based Action Recognition
on UAV-Human
Fine-Grained Named Entity Recognition using ELMo and Wikidata
Fine-grained Named Entity Recognition is a task whereby we detect and classify entity mentions to a large set of types.
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems.
Realizing data features by deep nets
Based on refined covering number estimates, we find that, to realize some complex data features, deep nets can improve the performances of shallow neural networks (shallow nets for short) without requiring additional capacity costs.
Generalization Properties of hyper-RKHS and its Applications
This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the introduced regression models in an approximation theory view.
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.
Ranked #7 on
3D Action Recognition
on Assembly101
Fast and Strong Convergence of Online Learning Algorithms
In this paper, we study the online learning algorithm without explicit regularization terms.
Convergence of Unregularized Online Learning Algorithms
In this paper we study the convergence of online gradient descent algorithms in reproducing kernel Hilbert spaces (RKHSs) without regularization.
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
Regularization kernel network is an effective and widely used method for nonlinear regression analysis.
Real-time 3D Human Tracking for Mobile Robots with Multisensors
An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D).
Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction
Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information from both regular and saturated measurements.
Latent Topic Embedding
Topic modeling and word embedding are two important techniques for deriving latent semantics from data.
A Novel Artificial Fish Swarm Algorithm for Pattern Recognition with Convex Optimization
Among these parameters, visual and step are very significant in view of the fact that artificial fish basically move based on these parameters.
Functional Hashing for Compressing Neural Networks
As the complexity of deep neural networks (DNNs) trend to grow to absorb the increasing sizes of data, memory and energy consumption has been receiving more and more attentions for industrial applications, especially on mobile devices.
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks
As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples.
Place classification with a graph regularized deep neural network model
Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.
Pinball Loss Minimization for One-bit Compressive Sensing: Convex Models and Algorithms
The one-sided $\ell_1$ loss and the linear loss are two popular loss functions for 1bit-CS.
Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform
This paper describes the solution of Bazinga Team for Tmall Recommendation Prize 2014.
Sparse Additive Text Models with Low Rank Background
This paper extends to propose sparse additive model with low rank background (SAM-LRB), and simple yet efficient estimation.
Bayesian Probabilistic Co-Subspace Addition
Furthermore, PCSA is extended to tackling and filling missing values, to adapting its sparseness, and to modelling tensor data.
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling
Here we propose a simple mechanism for Bayesian inference which involves averaging over a few feature detection neurons which fire at a rate determined by their similarity to a sensory stimulus.
