Search Results for author:
Found 45 papers, 14 papers with code
Max-Entropy Feed-Forward Clustering Neural Network
Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions.
Margin-Based Feed-Forward Neural Network Classifiers
Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects.
TransA: An Adaptive Approach for Knowledge Graph Embedding
Knowledge representation is a major topic in AI, and many studies attempt to represent entities and relations of knowledge base in a continuous vector space.
TransG : A Generative Mixture Model for Knowledge Graph Embedding
Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence.
From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction
Knowledge graph embedding aims at offering a numerical knowledge representation paradigm by transforming the entities and relations into continuous vector space.
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
To this end, this paper proposes a semantic representation method for knowledge graph \textbf{(KSR)}, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple.
KSR: A Semantic Representation of Knowledge Graph within a Novel Unsupervised Paradigm
Since both aspects and categories are semantics-relevant, the collection of categories in each aspect is treated as the semantic representation of this triple.
SAR: Semantic Analysis for Recommendation
Recommendation system is a common demand in daily life and matrix completion is a widely adopted technique for this task.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70, 000 fashion products from 10 categories, with 7, 000 images per category.
Hungarian Layer: Logics Empowered Neural Architecture
However, lacking of logic flow (e. g. \textit{if, for, while}), traditional algorithms (e. g. \textit{Hungarian algorithm, A$^*$ searching, decision tress algorithm}) could not be embedded into this paradigm, which limits the theories and applications.
NDT: Neual Decision Tree Towards Fully Functioned Neural Graph
Though traditional algorithms could be embedded into neural architectures with the proposed principle of \cite{xiao2017hungarian}, the variables that only occur in the condition of branch could not be updated as a special case.
Intelligence Graph
However, to construct powerful intelligence systems with various methods, we propose the intelligence graph (short as \textbf{\textit{iGraph}}), which is composed by both of neural and probabilistic graph, under the framework of forward-backward propagation.
Convexification of Neural Graph
Traditionally, most complex intelligence architectures are extremely non-convex, which could not be well performed by convex optimization.
Dual Ask-Answer Network for Machine Reading Comprehension
The task of question answering or question generation aims to infer an answer or a question when given the counterpart based on context.
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
Asynchronous "Events" are Better For Motion Estimation
As an instance of event-based camera, Dynamic and Active-pixel Vision Sensor (DAVIS) combines a standard camera and an event-based camera.
Matrix Completion using Kronecker Product Approximation
A matrix completion problem is to recover the missing entries in a partially observed matrix.
KoPA: Automated Kronecker Product Approximation
Specifically, we propose to approximate a given matrix by the sum of a few Kronecker products of matrices, which we refer to as the Kronecker product approximation (KoPA).
Hybrid Kronecker Product Decomposition and Approximation
As an effective dimension reduction tool, singular value decomposition is often used to analyze high dimensional matrices, which are traditionally assumed to have a low rank matrix approximation.
Searching for polarization in signed graphs: a local spectral approach
In this paper we formulate the problem of finding local polarized communities in signed graphs as a locally-biased eigen-problem.
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback
In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group.
Generalizable Mixed-Precision Quantization via Attribution Rank Preservation
On the contrary, our GMPQ searches the mixed-quantization policy that can be generalized to largescale datasets with only a small amount of data, so that the search cost is significantly reduced without performance degradation.
Support Vector Machines under Adversarial Label Contamination
Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attacks have not yet been widely understood.
AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback
This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AIWork Group, where the framework of the eigenvector-based channel state information (CSI) feedback problem is firstly provided.
Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search
Differentiable architecture search (DARTS) acquires the optimal architectures by optimizing the architecture parameters with gradient descent, which significantly reduces the search cost.
Concise and interpretable multi-label rule sets
Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability.
Token-Label Alignment for Vision Transformers
Data mixing strategies (e. g., CutMix) have shown the ability to greatly improve the performance of convolutional neural networks (CNNs).
A Knowledge-Driven Meta-Learning Method for CSI Feedback
Recently, deep learning (DL) has been introduced to enhance CSI feedback in massive MIMO application, where the massive collected training data and lengthy training time are costly and impractical for realistic deployment.
Learning Accurate Performance Predictors for Ultrafast Automated Model Compression
On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation.
HiFi: High-Information Attention Heads Hold for Parameter-Efficient Model Adaptation
To fully leverage the advantages of large-scale pre-trained language models (PLMs) on downstream tasks, it has become a ubiquitous adaptation paradigm to fine-tune the entire parameters of PLMs.
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks
However, with the growth of model scale and the rising number of downstream tasks, this paradigm inevitably meets the challenges in terms of computation consumption and memory footprint issues.
ImageBind-LLM: Multi-modality Instruction Tuning
During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder.
Knowledge-driven Meta-learning for CSI Feedback
In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase.
Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information.
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings.
Ranked #2 on
Visual Question Answering
on BenchLMM
Weaver: Foundation Models for Creative Writing
Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models.
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings
We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language.
Look Before You Leap: Towards Decision-Aware and Generalizable Tool-Usage for Large Language Models
Meanwhile, we propose a novel tool sampling strategy to enhance the generalizability of LLMs over unseen tools.
Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention
Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust.
Maximizing Energy Charging for UAV-assisted MEC Systems with SWIPT
By leveraging the SWIPT technique, the UAV can simultaneously transmit energy and the computing results during the downlink period.
Visual CoT: Unleashing Chain-of-Thought Reasoning in Multi-Modal Language Models
This paper presents Visual CoT, a novel pipeline that leverages the reasoning capabilities of multi-modal large language models (MLLMs) by incorporating visual Chain-of-Thought (CoT) reasoning.
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning.
