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Found 29 papers, 5 papers with code
Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning
Distantly supervised relation extraction is widely used in the construction of knowledge bases due to its high efficiency.
Rethinking Information Loss in Medical Image Segmentation with Various-sized Targets
Medical image segmentation presents the challenge of segmenting various-size targets, demanding the model to effectively capture both local and global information.
Joint Sparse Estimation with Cardinality Constraint via Mixed-Integer Semidefinite Programming
To further reduce the computation time in such scenarios, a relaxation-based approach can be employed to obtain an approximate solution of the MISDP reformulation, at the expense of a reduced estimation performance.
LEMON: Lossless model expansion
Our empirical results demonstrate that LEMON reduces computational costs by 56. 7% for Vision Transformers and 33. 2% for BERT when compared to training from scratch.
Let Models Speak Ciphers: Multiagent Debate through Embeddings
Although natural language is an obvious choice for communication due to LLM's language understanding capability, the token sampling step needed when generating natural language poses a potential risk of information loss, as it uses only one token to represent the model's belief across the entire vocabulary.
Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method
The past decade has witnessed great strides in video recovery by specialist technologies, like video inpainting, completion, and error concealment.
Multi-Modality Guidance Network For Missing Modality Inference
Multimodal models have gained significant success in recent years.
Taxonomy-Structured Domain Adaptation
Domain adaptation aims to mitigate distribution shifts among different domains.
Machine Learning Force Fields with Data Cost Aware Training
To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
Label Inference Attack against Split Learning under Regression Setting
In this work, we step further to study the leakage in the scenario of the regression model, where the private labels are continuous numbers (instead of discrete labels in classification).
UGformer for Robust Left Atrium and Scar Segmentation Across Scanners
an enhanced transformer module with deformable convolutions to improve the blending of the transformer information with convolutional information and help predict irregular LAs and scar shapes.
Differentially Private Estimation of Hawkes Process
Point process models are of great importance in real world applications.
Differentially Private Multi-Party Data Release for Linear Regression
Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects.
Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities
Specifically, we design a tag encoding module to cover both the single modality and multiple modalities missing cases, so as to guide the network's attention to those missing modalities.
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably
We investigate the role of noise in optimization algorithms for learning over-parameterized models.
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation
Our experiments show that there is a trade-off between understanding tasks and generation tasks while using the same model, and a feasible way to improve both tasks is to use more data.
Accelerate Distributed Stochastic Descent for Nonconvex Optimization with Momentum
We propose a momentum method for such model averaging approaches.
Extended Successive Convex Approximation for Phase Retrieval with Dictionary Learning
The second algorithm, referred to as SCAphase, uses auxiliary variables and is favorable in the case of highly diverse mixture models.
Recovery under Side Constraints
Finally, we address the measurement system design for linear and nonlinear measurements of sparse signals.
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization
Numerous empirical evidences have corroborated the importance of noise in nonconvex optimization problems.
Regularized Attentive Capsule Network for Overlapped Relation Extraction
Distantly supervised relation extraction has been widely applied in knowledge base construction due to its less requirement of human efforts.
On Computation and Generalization of Generative Adversarial Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies.
Towards Understanding the Importance of Shortcut Connections in Residual Networks
We show, however, that gradient descent combined with proper normalization, avoids being trapped by the spurious local optimum, and converges to a global optimum in polynomial time, when the weight of the first layer is initialized at 0, and that of the second layer is initialized arbitrarily in a ball.
Towards Understanding the Importance of Noise in Training Neural Networks
Numerous empirical evidence has corroborated that the noise plays a crucial rule in effective and efficient training of neural networks.
Improving Abstractive Document Summarization with Salient Information Modeling
Comprehensive document encoding and salient information selection are two major difficulties for generating summaries with adequate salient information.
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning
Extracting relations is critical for knowledge base completion and construction in which distant supervised methods are widely used to extract relational facts automatically with the existing knowledge bases.
Ranked #1 on
Relationship Extraction (Distant Supervised)
on New York Times Corpus
(Average Precision metric)
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Asynchronous momentum stochastic gradient descent algorithms (Async-MSGD) is one of the most popular algorithms in distributed machine learning.
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization
Our theoretical discovery partially corroborates the empirical success of MSGD in training deep neural networks.
Implementation of Training Convolutional Neural Networks
Deep learning refers to the shining branch of machine learning that is based on learning levels of representations.
