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Found 20 papers, 11 papers with code
STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map
Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL).
AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages
The advancement of speech technologies has been remarkable, yet its integration with African languages remains limited due to the scarcity of African speech corpora.
Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality
To address this issue, we developed TrueImage 2. 0, an artificial intelligence (AI) model for assessing patient photo quality for telemedicine and providing real-time feedback to patients for photo quality improvement.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Clustering Plotted Data by Image Segmentation
We describe the method and compare it to ten other clustering methods on synthetic data to illustrate its advantages and disadvantages.
Meaningfully Debugging Model Mistakes using Conceptual Counterfactual Explanations
Understanding and explaining the mistakes made by trained models is critical to many machine learning objectives, such as improving robustness, addressing concept drift, and mitigating biases.
Persistent Anti-Muslim Bias in Large Language Models
It has been observed that large-scale language models capture undesirable societal biases, e. g. relating to race and gender; yet religious bias has been relatively unexplored.
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics.
Improving Training on Noisy Stuctured Labels
Systematic experiments on image segmentation and text tagging demonstrate the strong performance of ECN in improving training on noisy structured labels.
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
Their feedback identified that Gradio should support a variety of interfaces and frameworks, allow for easy sharing of the interface, allow for input manipulation and interactive inference by the domain expert, as well as allow embedding the interface in iPython notebooks.
Contrastive Variational Autoencoder Enhances Salient Features
The cVAE explicitly models latent features that are shared between the datasets, as well as those that are enriched in one dataset relative to the other, which allows the algorithm to isolate and enhance the salient latent features.
Concrete Autoencoders for Differentiable Feature Selection and Reconstruction
We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and simultaneously learns a neural network to reconstruct the input data from the selected features.
Ranked #1 on
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on Fashion-MNIST
Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Edit Distance, Euclidean, etc.
Contrastive Multivariate Singular Spectrum Analysis
We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data.
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Euclidean, and edit distance.
Stochastic EM for Shuffled Linear Regression
We consider the problem of inference in a linear regression model in which the relative ordering of the input features and output labels is not known.
INTERPRETATION OF NEURAL NETWORK IS FRAGILE
In this paper, we show that interpretation of deep learning predictions is extremely fragile in the following sense: two perceptively indistinguishable inputs with the same predicted label can be assigned very different}interpretations.
Interpretation of Neural Networks is Fragile
In this paper, we show that interpretation of deep learning predictions is extremely fragile in the following sense: two perceptively indistinguishable inputs with the same predicted label can be assigned very different interpretations.
Contrastive Principal Component Analysis
We present a new technique called contrastive principal component analysis (cPCA) that is designed to discover low-dimensional structure that is unique to a dataset, or enriched in one dataset relative to other data.
Linear Regression with Shuffled Labels
We study the regimes in which each estimator excels, and generalize the estimators to the setting where partial ordering information is available in the form of experiments replicated independently.
