Search Results for author:
Found 24 papers, 5 papers with code
Harnessing the Power of Semi-Structured Knowledge and LLMs with Triplet-Based Prefiltering for Question Answering
This distinguishes it from related methods that are purely based on latent representations.
10 Years of Fair Representations: Challenges and Opportunities
In this paper, we look back at the first ten years of FRL by i) revisiting its theoretical standing in light of recent work in deep learning theory that shows the hardness of removing information in neural network representations and ii) presenting the results of a massive experimentation (225. 000 model fits and 110. 000 AutoML fits) we conducted with the objective of improving on the common evaluation scenario for FRL.
Amplifying Exploration in Monte-Carlo Tree Search by Focusing on the Unknown
It strategically allocates computational resources to focus on promising segments of the search tree, making it a very attractive search algorithm in large search spaces.
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
Eventually, we analyze the learning behavior of the peers and observe their ability to rank the agents' performance within the study group and understand which agents give reliable advice.
Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents.
Neural RELAGGS
We demonstrate the increased predictive performance by comparing N-RELAGGS with RELAGGS and multiple other state-of-the-art algorithms.
A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction
One important parameter is the depth of integration: the point at which the latent representations are computed or merged, which can be either early, intermediate, or late.
Invariant Representations with Stochastically Quantized Neural Networks
In this paper, we propose a methodology for direct computation of the mutual information between a neural layer and a sensitive attribute.
Fair Interpretable Representation Learning with Correction Vectors
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information.
Fair Interpretable Learning via Correction Vectors
Neural network architectures have been extensively employed in the fair representation learning setting, where the objective is to learn a new representation for a given vector which is independent of sensitive information.
Fast Private Parameter Learning and Inference for Sum-Product Networks
A sum-product network (SPN) is a graphical model that allows several types of inferences to be drawn efficiently.
Pattern Sampling for Shapelet-based Time Series Classification
The asymptotic time complexity of subsequence-based algorithms remains a higher-order polynomial, because these algorithms are based on exhaustive search for highly discriminative subsequences.
Focusing Knowledge-based Graph Argument Mining via Topic Modeling
We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata, building a graph based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence.
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series
We show that, because DNNs can find useful abstract feature representations, they can be used when prior knowledge about the signal-generating process exists, but is not complete, as it is particularly the case in "new-physics" searches.
Deep Unsupervised Identification of Selected SNPs between Adapted Populations on Pool-seq Data
The exploration of selected single nucleotide polymorphisms (SNPs) to identify genetic diversity between different sequencing population pools (Pool-seq) is a fundamental task in genetic research.
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation
Most deep neural networks are considered to be black boxes, meaning their output is hard to interpret.
Ranking Creative Language Characteristics in Small Data Scenarios
We therefore adapt the DirectRanker to provide a new deep model for ranking creative language with small data.
Secure Sum Outperforms Homomorphic Encryption in (Current) Collaborative Deep Learning
Deep learning (DL) approaches are achieving extraordinary results in a wide range of domains, but often require a massive collection of private data.
Towards Probability-based Safety Verification of Systems with Components from Machine Learning
As a result, the quantitatively determined bound on the probability of a classification error of an ML component in a safety-critical system contributes in a well-defined way to the latter's overall safety verification.
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture.
Online Multi-Label Classification: A Label Compression Method
Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization.
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational Costs
Using random sampling reduces the number of evaluated candidates and consequently the required computational cost, while the classification accuracy of the resulting models is also not significantly different than that of the exact algorithm.
Graph Clustering with Density-Cut
We demonstrate that our method has several attractive benefits: (a) Dcut provides an intuitive criterion to evaluate the goodness of a graph clustering in a more natural and precise way; (b) Built upon the density-connected tree, Dcut allows identifying the meaningful graph clusters of densely connected vertices efficiently; (c) The density-connected tree provides a connectivity map of vertices in a graph from a local density perspective.
Social and Information Networks Physics and Society
