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Found 24 papers, 9 papers with code
DANGNT-SGU at SemEval-2022 Task 11: Using Pre-trained Language Model for Complex Named Entity Recognition
In this paper, we describe a system that we built to participate in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition, specifically the track Mono-lingual in English.
DeepCoDA: personalized interpretability for compositional health
Interpretability allows the domain-expert to directly evaluate the model's relevance and reliability, a practice that offers assurance and builds trust.
Active Level Set Estimation for Continuous Search Space with Theoretical Guarantee
When the function is black-box and expensive to evaluate, the level sets need to be found in a minimum set of function evaluations.
Variational Flow Models: Flowing in Your Style
We introduce "posterior flows" - generalizations of "probability flows" to a broader class of stochastic processes not necessarily diffusion processes - and propose a systematic training-free method to transform the posterior flow of a "linear" stochastic process characterized by the equation Xt = at * X0 + st * X1 into a straight constant-speed (SC) flow, reminiscent of Rectified Flow.
Revisiting the Dataset Bias Problem from a Statistical Perspective
To overcome this challenge, we propose to approximate \frac{1}{p(u|b)} using a biased classifier trained with "bias amplification" losses.
Pragmatic Radiology Report Generation
When pneumonia is not found on a chest X-ray, should the report describe this negative observation or omit it?
Real-Time Magnetic Tracking and Diagnosis of COVID-19 via Machine Learning
In this work, we fused magnetic respiratory sensing technology (MRST) with machine learning (ML) to create a diagnostic platform for real-time tracking and diagnosis of COVID-19 and other respiratory diseases.
Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
Recently, multimodal contrastive learning (MMCL) approaches, such as CLIP, have achieved a remarkable success in learning representations that are robust against distribution shift and generalize to new domains.
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Despite being efficient, Max-SW and its amortized version cannot guarantee metricity property due to the sub-optimality of the projected gradient ascent and the amortization gap.
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Since the EMA generator can be considered as an ensemble of the generator's old versions and often undergoes a smaller change in updates compared to the generator, training on its synthetic samples can help the student recall the past knowledge and prevent the student from adapting too quickly to new updates of the generator.
Efficient Classification with Counterfactual Reasoning and Active Learning
Data augmentation is one of the most successful techniques to improve the classification accuracy of machine learning models in computer vision.
Black-box Few-shot Knowledge Distillation
Traditional KD methods require lots of labeled training samples and a white-box teacher (parameters are accessible) to train a good student.
Fast Conditional Network Compression Using Bayesian HyperNetworks
We introduce a conditional compression problem and propose a fast framework for tackling it.
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
Trojan attacks on deep neural networks are both dangerous and surreptitious.
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks
To address this issue, we propose a novel model fusion framework, named CLAFusion, to fuse neural networks with a different number of layers, which we refer to as heterogeneous neural networks, via cross-layer alignment.
Improving Mini-batch Optimal Transport via Partial Transportation
Mini-batch optimal transport (m-OT) has been widely used recently to deal with the memory issue of OT in large-scale applications.
Hub and Spoke Logistics Network Design for Urban Region with Clustering-Based Approach
This study aims to propose effective modeling and approach for designing a logistics network in the urban area in order to offer an efficient flow distribution network as a competitive strategy in the logistics industry where demand is sensitive to both price and time.
On Transportation of Mini-batches: A Hierarchical Approach
To address these problems, we propose a novel mini-batch scheme for optimal transport, named Batch of Mini-batches Optimal Transport (BoMb-OT), that finds the optimal coupling between mini-batches and it can be seen as an approximation to a well-defined distance on the space of probability measures.
Bayesian Optimization with Missing Inputs
In real-world applications, BO often faces a major problem of missing values in inputs.
DeepCoDA: personalized interpretability for compositional health data
We define personalized interpretability as a measure of sample-specific feature attribution, and view it as a minimum requirement for a precision health model to justify its conclusions.
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables.
Stable Bayesian Optimisation via Direct Stability Quantification
In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions.
UIT-DANGNT-CLNLP at SemEval-2017 Task 9: Building Scientific Concept Fixing Patterns for Improving CAMR
This paper describes the improvements that we have applied on CAMR baseline parser (Wang et al., 2016) at Task 8 of SemEval-2016.
Control Matching via Discharge Code Sequences
In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220, 000 patients.
