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Found 37 papers, 17 papers with code
How Will I Argue? A Dataset for Evaluating Recommender Systems for Argumentations
Exchanging arguments is an important part in communication, but we are often flooded with lots of arguments for different positions or are captured in filter bubbles.
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
In this work, we present two different ways to leverage LLM-generated synthetic data to train and improve stance detection agents for online political discussions: first, we show that augmenting a small fine-tuning dataset with synthetic data can improve the performance of the stance detection model.
AQuA -- Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs
In this work, we introduce AQuA, an additive score that calculates a unified deliberative quality score from multiple indices for each discussion post.
Just Cluster It: An Approach for Exploration in High-Dimensions using Clustering and Pre-Trained Representations
In this paper we adopt a representation-centric perspective on exploration in reinforcement learning, viewing exploration fundamentally as a density estimation problem.
Backward Learning for Goal-Conditioned Policies
Can we learn policies in reinforcement learning without rewards?
Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data
A CT scanner consists of an X-ray source that is spun around an object of interest.
Cyclophobic Reinforcement Learning
In environments with sparse rewards, finding a good inductive bias for exploration is crucial to the agent's success.
A Survey on Self-Supervised Representation Learning
Learning meaningful representations is at the heart of many tasks in the field of modern machine learning.
Transformer-based World Models Are Happy With 100k Interactions
Deep neural networks have been successful in many reinforcement learning settings.
Model-based Reinforcement Learning
reinforcement-learning
+1
Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm
Very large state spaces with a sparse reward signal are difficult to explore.
Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images
In the context of the NeurIPS 22 Cell Segmentation Challenge, the proposed solution is shown to generalize well in a multi-modality setting, while respecting domain-specific requirements such as focusing on specific cell types.
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Many production processes are characterized by numerous and complex cause-and-effect relationships.
Blindly Deconvolving Super-noisy Blurry Image Sequences
In this paper, we consider multi-frame blind deconvolution (MFBD), where image blur is described by the convolution of an unobservable, undeteriorated image and an unknown filter, and the objective is to recover the undeteriorated image from a sequence of its blurry and noisy observations.
Optimizing Intermediate Representations of Generative Models for Phase Retrieval
With extensive experiments on the Fourier phase retrieval problem and thorough ablation studies, we can show the benefits of our modified ILO and the new initialization schemes.
Deblurring Photographs of Characters Using Deep Neural Networks
Our method consists of three steps: First, we estimate a warping transformation of the images to align the sharp images with the blurred ones.
A Closer Look at Reference Learning for Fourier Phase Retrieval
In this work, we consider a modified version of the phase retrieval problem, which allows for a reference image to be added onto the image before the Fourier magnitudes are measured.
Non-Iterative Phase Retrieval With Cascaded Neural Networks
Fourier phase retrieval is the problem of reconstructing a signal given only the magnitude of its Fourier transformation.
Learning to Plan via a Multi-Step Policy Regression Method
We propose a new approach to increase inference performance in environments that require a specific sequence of actions in order to be solved.
Contour Proposal Networks for Biomedical Instance Segmentation
We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities.
2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks
By solving the brain mapping problem on this graph using graph neural networks, we obtain significantly improved classification results.
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections
Cytoarchitectonic maps provide microstructural reference parcellations of the brain, describing its organization in terms of the spatial arrangement of neuronal cell bodies as measured from histological tissue sections.
Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale
Here we present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains.
Smaller World Models for Reinforcement Learning
Sample efficiency remains a fundamental issue of reinforcement learning.
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Fast Multi-Level Foreground Estimation
Alpha matting aims to estimate the translucency of an object in a given image.
PyMatting: A Python Library for Alpha Matting
Alpha matting describes the problem of separating the objects in the foreground from the background of an image given only a rough sketch.
Phase Retrieval Using Conditional Generative Adversarial Networks
In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems.
On the Vulnerability of Capsule Networks to Adversarial Attacks
This paper extensively evaluates the vulnerability of capsule networks to different adversarial attacks.
Modular Block-diagonal Curvature Approximations for Feedforward Architectures
We propose a modular extension of backpropagation for the computation of block-diagonal approximations to various curvature matrices of the training objective (in particular, the Hessian, generalized Gauss-Newton, and positive-curvature Hessian).
Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks
We show that the self-supervised model has implicitly learned to distinguish several cortical brain areas -- a strong indicator that the proposed auxiliary task is appropriate for cytoarchitectonic mapping.
Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks
Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks.
Attribute-Based Classification for Zero-Shot Visual Object Categorization
To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object’s color or shape.
On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit
We establish a link between Fourier optics and a recent construction from the machine learning community termed the kernel mean map.
A Machine Learning Approach for Non-blind Image Deconvolution
In this work, we also rely on a two-step procedure, but learn the second step on a large dataset of natural images, using a neural network.
Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake
Modelling camera shake as a space-invariant convolution simplifies the problem of removing camera shake, but often insufficiently models actual motion blur such as those due to camera rotation and movements outside the sensor plane or when objects in the scene have different distances to the camera.
How to Explain Individual Classification Decisions
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data.
