D2KLab's approach predicts tweet engagement using two stages.
problem Predicting user engagement with tweets.
method Two-stage approach: feature learning and ensemble XGBoost.
result Ranked 22 in the 2020 RecSys Challenge leaderboard.
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48 results for “2020 RecSys Challenge”
Twitter releases dataset to study user engagement on Home Timeline, focusing on privacy.
problem Limited exploration of user feedback on Twitter's Home Timeline.
method Release of a large-scale dataset of 160 million Tweets with engagement information.
result Demonstrates challenges in predicting user engagements and privacy concerns.
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical obs…
First ABAW 2020 Competition analyzes affective behavior tasks.
problem Automatic analysis of valence-arousal, basic expressions, and action units in real-world scenarios.
method Provided Aff-Wild2 database, described Challenges, evaluation metrics, and top-performing systems.
result Demonstrated the feasibility of automatic affective behavior analysis in real-world settings.
XPDNet wins MRI reconstruction challenge with neural network.
problem MRI reconstruction from under-sampled data.
method Inspired by MRI and computer vision best practices, XPDNet uses a neural network.
result XPDNet achieved state-of-the-art results in the 2020 fastMRI challenge.
Bayesian optimization outperformed random search in machine learning hyperparameter tuning challenge.
problem Optimizing hyperparameters of machine learning models using derivative-free methods.
method Bayesian optimization vs. random search on real datasets.
result Bayesian optimization significantly outperformed random search in held-out objective functions.
We learn hierarchical slate representations for collaborative filtering.
problem Building models for recommendation systems with hierarchical slates.
method Learning low-dimensional embeddings of hierarchical slates using recursive composition rules.
result Improved recommendation system performance on a real-world dataset.
Bayesian SHMM models speech units from unannotated speech.
problem Discover latent speech units from unannotated data.
method Bayesian Subspace Hidden Markov Model (SHMM).
result System performs well on synthesis quality, lower bitrate.
CVPR 2020 challenge evaluates continual learning in computer vision.
problem Making AI systems more adaptive and efficient.
method Evaluated different continual learning approaches on a common hardware with shared metrics.
result Reported main results, finalists, and prize winners.
RoBERTa model detects counterfactual statements in text.
problem Detecting and extracting counterfactual statements from text.
method Used RoBERTa language representation model for both subtasks.
result RoBERTa achieved top performance in both subtasks at SemEval-2020.
ADReSS Challenge at INTERSPEECH 2020 benchmarks speech recognition for Alzheimer's dementia.
problem Automated recognition of Alzheimer's dementia from spontaneous speech.
method Provides a benchmark speech dataset, defines two tasks (classification and regression), and presents baseline models.
result Demonstrates the feasibility of automated speech recognition for Alzheimer's dementia.
A new method detects anomalous sounds using self-supervised learning.
problem Detecting anomalous sounds in noisy data.
method Flow-based self-supervised density estimation using Normalizing Flows.
result The method improves anomaly detection performance significantly.
NIST's CTS Challenge evaluates speaker recognition using telephony data.
problem Improving speaker recognition accuracy using telephony data.
method Large-scale neural network architectures, extensive data augmentation, proprietary data, and fine-tuning.
result Remarkable improvements in speaker recognition performance.
Squirrel switches between optimizers for better performance.
problem Finding the best optimizer for a given problem.
method Switches between different optimizers based on performance.
result Improves performance on various problems.
FinCausal 2020 task detects financial document causality.
problem Detect causal relationships in financial documents.
method Binary classification and relation extraction tasks.
result 16 teams participated, 13 submitted system descriptions.
Many-to-one RNN predicts user hotel clicks from browsing history.
problem Predicting user hotel clicks from browsing history.
method Combines rule-based algorithm with Gated RNN to sort accommodations.
result Promising results but computationally demanding.
Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approachcs.LG
A neural collaborative filtering method predicts corn hybrid yield performance.
problem Predicting yield performance of untested hybrid combinations in plant breeding.
method Ensemble of matrix factorization and neural networks.
result The model significantly outperformed other models in the Syngenta Crop Challenge.
Boosting trees predict Twitch subscriptions from user activity.
problem Predicting Twitch user subscriptions from activity data.
method Used boosting trees and target-encodings for high cardinality categoricals.
result User activity can be better predicted than content alone.
Study reveals the 2020 U.S. stock crash was endogenous, not caused by COVID.
problem Understanding the cause of the 2020 U.S. stock market crash.
method Applied log-periodic power law singularity (LPPLS) methodology to analyze four major U.S. stock market indexes.
result The 2020 U.S. stock market crash was endogenous, stemming from systemic instability, not COVID.
Study shows simple vector quantization measures correlate with deep learning generalization.
problem Understanding and predicting generalization in deep learning models.
method Applying complexity measures from approximation and information theory to deep learning features.
result Simple vector quantization measures correlate well with generalization performance in deep learning.
Paper discusses ASD challenge for machine condition monitoring.
problem Detecting unknown anomalous sounds without labeled data.
method Design and evaluation of a large-scale ASD dataset, novel approaches.
result Several novel approaches developed, evaluation results analyzed.
Recent pruning methods at initialization fall short of random pruning's accuracy.
problem Improving neural network accuracy through pruning at initialization.
method Various pruning methods (SNIP, GraSP, SynFlow, magnitude pruning) are evaluated; per-layer pruning decisions are proposed.
result Randomly shuffling or sampling initial weights preserves or improves accuracy, suggesting challenges with pruning heuristics.
This paper presents a method to improve continual learning stability and plasticity.
problem Balancing learning stability and plasticity in deep learning.
method Batch-level Experience Replay with Review approach.
result Achieved 1st place in all three scenarios of the CVPR 2020 CLVision challenge.
India's 2020-21 GDP growth forecast is projected at 1.9% due to COVID-19.
problem Impact of COVID-19 on India's 2020-21 GDP growth forecast.
method Quarterly GVA estimates using capacity utilisation assumptions and 2019-20 data.
result India's GDP growth in 2020-21 is expected to be 1.9%.
The paper analyzes the implicit bias of SGD near loss manifold and provides new insights.
problem Understanding the implicit bias of SGD near loss manifolds in overparametrized models.
method Adapting ideas from Katzenberger (1991) to analyze SGD dynamics using a stochastic differential equation (SDE).
result SGD with label noise locally decreases the sharpness of loss, leading to a global analysis of implicit bias.
This paper shows how to approximate any log-concave distribution using well-conditioned affine coupling flows.
problem Understanding the representational power of affine coupling flows for log-concave distributions.
method Leveraging connections between affine coupling architectures, Langevin dynamics, and Hénon maps to prove log-concave approximation.
result Any log-concave distribution can be approximated using well-conditioned affine-coupling flows.
GeoLifeCLEF 2020 dataset pairs species observations with environmental data.
problem Understanding geographic species distribution.
method Presented a dataset of species observations with environmental features.
result Advances in location-based species recommendation.
System tackles indeterminacies in automated audio captioning.
problem Word selection and sentence length indeterminacies in automated audio captioning.
method Solves caption generation and sub-indeterminacy problems through multi-task learning to estimate keywords and sentence length.
result Model achieved 20.7 SPIDEr score, significantly outperforming baseline.
New gradient estimators for discrete variables improve model training.
problem Training models with discrete latent variables is challenging due to high gradient variance.
method Introduced novel gradient estimators based on importance sampling and statistical couplings, extending to categorical variables.
result Proposed gradient estimators outperform previous methods in systematic experiments.
Fuzzy Forests reduces feature space in high-dimensional survey data.
problem High-dimensional and highly correlated datasets in social science.
method Fuzzy Forests algorithm, a variant of Random Forests.
result Partisan polarization was the strongest factor in the 2020 presidential election.
PowRL uses RL to manage power grids robustly, reducing overloads and maintaining power reliability.
problem Managing transient stability and preventing blackouts in power networks with uncertain generation and load demands.
method PowRL leverages a novel heuristic for overload management and RL-guided topology selection to ensure safe and reliable operation.
result PowRL outperforms other agents in L2RPN challenges, demonstrating robust performance in various scenarios.
Study reveals 2020 stock crashes were mostly endogenous, not exogenous.
problem Identifying the cause of the 2020 global stock market crash.
method Applied log-periodic power law singularity (LPPLS) methodology to analyze stock market indexes.
result The 2020 stock market crashes were mostly endogenous, driven by systemic instability.
Simple model outperforms neural networks on language understanding tasks.
problem Neural networks struggle with creating novel expressions from familiar ones.
method Attention-inspired modification of a baseline model, focusing on sequential thinking and acting.
result Simple model achieves good performance on gSCAN tasks, validating the benchmark.
2020 Census uses more noise to protect privacy than needed, improving data accuracy.
problem Ensuring privacy in census data while maintaining accuracy for policy decisions.
method Applied -differential privacy to track and reduce noise across geographical levels.
result The 2020 Census provides stronger privacy protections than its nominal guarantees suggest.
Deep RL agent secures 2nd place in CityLearn Challenge for district demand management.
problem Optimizing electrical demand of diverse buildings in a district.
method Centralised 'Soft Actor Critic' deep reinforcement learning agent.
result Achieved an averaged score of 0.967 on challenge dataset.
SLAM-net learns to navigate visually in challenging indoor environments.
problem Challenges in SLAM for visual robot navigation, especially in noisy conditions.
method Differentiable SLAM Network (SLAM-net) that encodes a particle filter SLAM algorithm in a differentiable graph and learns components through backpropagation.
result Significantly outperforms ORB-SLAM in noisy conditions and improves the Habitat Challenge 2020 PointNav task.
Challenge forecasts EV charging station usage accurately.
problem Better understanding of EV charging behaviors needed.
method Hierarchical forecasting of EV charging station occupancy.
result Hierarchical forecasting improves EV charging station occupancy predictions.
Researchers discuss connections and distinctions between DRITR and Kallus' work, focusing on policy evaluation and efficiency.
problem Policy evaluation and efficiency under distributionally robust individualized treatment rules.
method Efficient policy evaluation using combined data and DRITR under varying sample sizes.
result Efficient value function estimates can deliver competitive performance under certain conditions but not always.
New bound improves on weighted majority vote risk estimation.
problem Improving risk estimation for weighted majority vote.
method Novel Chebyshev-Cantelli inequality and PAC-Bayes-Bennett inequality.
result New bounds improve on existing methods.
A Sieve-Accelerated Quadrature Method for Exact Privacy Accounting in the 2020 U.S. Decennial Censuscs.CR
Efficiently calculates privacy guarantees for 2020 Census data.
problem Evaluate privacy guarantees for 2020 U.S. Census data releases.
method Sieve-accelerated quadrature method to evaluate tail probabilities of high-dimensional convolutions.
result Achieves 1,824-fold speedup over prior methods while maintaining error tolerances.
Microdata improves inflation forecasts after major shocks, study finds.
problem Forecasting inflation in a non-stationary environment with microeconomic data.
method Developed a scan test to detect periods of micro forecast outperformance, combined with adaptive machine learning.
result Micro forecasts improve inflation predictions after major shocks, especially after 2020.
Neural Architecture Search remains a very challenging meta-learning problem. Several recent techniques based on parameter-sharing idea have focused on reducing the NAS running time by leveraging proxy models, leading to architectures with competitive performance compared to those with hand-crafted designs. In this pape…
Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the perspective of first response organisations like UNICEF. Two main challenges are rapid access to data, and the ability…
DCASE 2021 ASD task tackles domain-shifted anomalous sound detection.
problem Detecting unknown anomalous sounds under domain-shifted conditions.
method Ensemble of outlier exposure and inlier modeling detectors, feature learning from machine identification.
result Two types of remarkable approaches were adopted by top teams.
Study analyzes market co-movements in critical mineral investments using change point detection and cross-sectional analysis.
problem Market dynamics in critical mineral investments during significant global events.
method Combines change-point detection (PELT algorithm) with cross-sectional analysis on ESG-ranked ETFs.
result Investors herded during market downturns and shifted to anti-herding after positive news and geopolitical shocks.
Improved algorithm for logistic bandits with better regret bounds.
problem Understanding the impact of non-linearity in logistic bandits.
method Introducing a new algorithm and providing refined analysis.
result Improved regret bounds scaling as in most favorable cases.
Improved exploration in adversarial MDPs via dilated bonuses.
problem Global exploration in adversarial MDPs with local-search policies.
method Dilated bonuses to facilitate global exploration.
result Improved regret bounds in various MDP settings.
Efficient algorithm for halfspaces with specific noise conditions.
problem Learning halfspaces with Massart and Tsybakov noise.
method PAC active learning algorithm for d-dimensional halfspaces.
result Near-optimal label complexity under Massart noise and lower than passive learning under Tsybakov noise.