Study evaluates predictive models for blended courses, analyzing performance across different offerings.
problem Limited success in predicting student performance in blended courses.
method Used data from two offerings of two different undergraduate courses to train and evaluate models.
result Models perform better on the same offering and less well on different offerings of the same course.
The most widely used activation functions in current deep feed-forward neural networks are rectified linear units (ReLU), and many alternatives have been successfully applied, as well. However, none of the alternatives have managed to consistently outperform the rest and there is no unified theory connecting properties…
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement lea…
Deformations of a Courant Algebroid E and its Dirac subbundle A have been widely considered under the assumption that the pseudo-Euclidean metric is fixed. In this paper, we attack the same problem in a setting that allows the pseudo-Euclidean metric to deform. Thanks to Roytenberg, a Courant algebroid is equivalent to…
New model improves volatility forecasting by reducing overestimation and underestimation.
problem SVR-GARCH model overestimates or underestimates volatility, hindering peak or trough behaviors.
method Proposes blending ARCH and augmented blending-ARCH models to improve volatility forecasting.
result Empirical results show improved volatility forecasting ability.
Study shows ethanol blends and incentives can significantly reduce transportation carbon emissions.
problem Rapid growth in electric vehicles requires complementary strategies to decarbonize transportation.
method Analysis of ethanol blending, regulatory incentives, and economic assessments.
result Ethanol blending, especially E15 and E85, can substantially reduce carbon emissions and provide economic benefits.
BayesBlend blends multiple models' predictions for better insurance loss predictions.
problem Improving insurance loss predictions by combining multiple models.
method Pseudo-Bayesian model averaging, stacking, and hierarchical stacking.
result BayesBlend provides a user-friendly way to blend model predictions and estimate weights.
Formalizes explanations as blending input and model output.
problem Creating clear and consistent explanations for model predictions.
method Defines properties of explanation functions and links them to model layers.
result Consistency of activations across layers implies consistency of explanations.
Proposes grade-aware course recommendation methods to improve student GPA.
problem Helping students select courses that lead to timely graduation and good grades.
method Two approaches: ranking courses by expected GPA impact and combining grade predictions with course recommendations.
result Grade-aware methods recommend courses leading to better student performance.
Quantum walks blend patterns into splines when averaged.
problem Understanding the asymptotic patterns of quantum random walks.
method Averaging over quantum coins using the Haar measure.
result Patterns blend into splines, showing a unified behavior.
This paper improves level generation using VAEs for coherent, logically following segments.
problem Generating coherent levels of non-fixed length and blending levels from different games.
method Sequential segment-based level generation using VAEs with a classifier for logical placement.
result Generated levels are more coherent and capable of blending levels from different games.
The paper proposes blending gradient boosted trees and neural networks for hierarchical time series forecasting.
problem Point and probabilistic forecasting of hierarchical time series.
method A blending methodology of gradient boosted trees and neural networks, with feature engineering and diverse model selection.
result Ranked within the gold medal range in both Accuracy and Uncertainty tracks of the M5 Competition.
New PCGML approach generates novel game content across multiple platformer domains.
problem Generating novel game content in new domains.
method Using a new affordance and path vocabulary, variational autoencoders trained on data from six platformer games produce new content with varying proportions of different domains.
result Captures latent level space spanning multiple domains and generates new content with varying proportions of different domains.
Proposes new models to predict student grades more accurately.
problem Accurately predicting future grades considering prior and concurrent courses.
method Context-aware, non-linear, and neural attentive models.
result Models outperform existing methods in predicting student grades.
The paper presents a framework for optimizing crypto-currency portfolios using generative models.
problem Optimizing crypto-currency portfolios using generative models.
method The approach involves evaluating diverse pairings of generative model forecasts and objective functions, using simulations and blending strategies.
result Eclectic blended portfolios outperform individual generative model-based portfolios.
Generative model blends query input with latent states for structured improvisation.
problem Generating structured music from latent states of a neural network.
method Used a Variational Autoencoder (VAE) trained on a specific style corpus, and controlled blending with a noisy channel.
result Generated music with longer-term structure that blends query input with network style.
Quantized deep neural networks (QDNNs) are attractive due to their much lower memory storage and faster inference speed than their regular full precision counterparts. To maintain the same performance level especially at low bit-widths, QDNNs must be retrained. Their training involves piecewise constant activation func…
NAK model predicts student grades by focusing on relevant past courses.
problem Accurately predicting future course grades for students.
method NAK model learns attention weights to focus on relevant past courses.
result NAK model outperforms CKRM and other methods in predicting grades.
A novel approach for augmenting histopathological images by blending Gaussian-Laplacian pyramids.
problem Data imbalance and inter-patient variability in histopathological images.
method Image blending using Gaussian-Laplacian pyramids to distribute inter-patient variability.
result Promising gains in performance compared to existing data augmentation techniques.
Paper improves neural network models for MOOC student course prediction.
problem Improving neural network-based predictive methods for MOOC student course trajectory modeling.
method Enhanced LSTM networks and Transformer architecture applied to MOOC student data.
result Improved accuracy in predicting MOOC student course trajectories.
Predicts student outcomes in real-time using domain adaptation.
problem Real-time student performance prediction in online courses.
method GritNet architecture with unsupervised domain adaptation.
result GritNet enhances real-time predictions, especially in early weeks.
Study examines Indian equity mutual funds' investment style and risk-shifting.
problem Understanding how Indian equity mutual funds' investment styles affect their returns.
method Estimating size and style beta coefficients, identifying breakpoints, analyzing investment styles, and assessing risk-shifting intensity.
result Funds can enhance returns by shifting to high-return styles like Small Value and Small Blend.
Advanced mathematical relativity course for math and physics students.
problem Mathematical foundations of general relativity.
method Comprehensive lecture notes covering advanced topics.
result Comprehensive coverage of mathematical relativity for advanced students.
Introduces scale calculus and M-polyfolds for graduate students.
problem No specific problem stated in the abstract.
method Lecture notes on scale calculus and M-polyfolds.
result No specific key result mentioned in the abstract.
OPERA blends multiple OPE estimators to evaluate new policies offline.
problem Lack of reliable offline policy evaluation methods for new policies.
method Adaptive blending of multiple OPE estimators without explicit selection.
result Consistent and reliable policy evaluation framework for offline RL.
Course on arithmetic lattices at EPFL.
problem Understanding arithmetic lattices.
method Introductory course on arithmetic lattices.
result Introduction to arithmetic lattices.
This book is a textbook for the basic course of differential geometry. It is recommended as an introductory material for this subject.
Discusses geometry problems for fun and learning.
problem Geometry problems and their solutions.
method Not original, but related to differential geometry course.
result Relation to differential geometry course.
Increasingly fast development and update cycle of online course contents, and diverse demographics of students in each online classroom, make student performance prediction in real-time (before the course finishes) and/or on curriculum without specific historical performance data available interesting topics for both i…
Modeling student course choices using latent variables.
problem Understanding student enrollment patterns in large universities.
method Probabilistic approach based on multilabel classification and mixture models.
result Demonstrated the model's ability to infer student interests guiding enrollment decisions.
AMEAN tackles BTDA by learning meta-sub-targets to bridge domain gaps and misalignments.
problem Blending-target Domain Adaptation (BTDA) with multiple sub-targets that are hard to distinguish.
method AMEAN uses two adversarial processes: first to align source and mixed target domains, second to learn meta-sub-targets.
result AMEAN significantly outperforms existing DA algorithms in BTDA scenarios.
A new method quantizes neural networks to low-precision without STE, improving accuracy.
problem Quantization of neural networks to low-precision without a complete theoretical understanding.
method Alpha-blending (AB) using stochastic gradient descent (SGD) to quantize weights and gradually increase the coefficient α. result Improves top-1 accuracy by 0.9% on 1-bit BinaryNet, 0.82% on 8-bit MobileNet v1, and 2.93% on 4-bit ResNet_50 v1/2 compared to STE.
In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoing courses, when the student outcomes are yet unknown, must rely on models trained from the historical data of previously offered courses. I…
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
problem Predicting Bitcoin price fluctuations in the cryptocurrency market.
method Integrates advanced ensemble learning methods, feature selection algorithms, and sentiment analysis.
result The GAS model demonstrates excellent performance in daily Bitcoin trend prediction.
Breiman's two cultures reconciled through blending statistical thinking.
problem Tension between parametric statistical and machine learning approaches.
method Establishing a link between parametric statistical and machine learning frameworks.
result Integrated statistical thinking can bridge the gap between two cultures.
Improved prediction of polymer morphology through machine learning and simulations.
problem Understanding and predicting the morphology of multi-component polymer blends.
method Modified Cahn-Hilliard model for simulations, machine learning for clustering and prediction.
result Machine learning achieved ≥ 90% accuracy in predicting polymer morphology. We introduce a new framework for training deep generative models for high-dimensional conditional density estimation. The Bottleneck Conditional Density Estimator (BCDE) is a variant of the conditional variational autoencoder (CVAE) that employs layer(s) of stochastic variables as the bottleneck between the input x a…
These are lecture notes on Floer and Rabinowitz-Floer homology written for a graduate course at UNICAMP August-December 2016 and a mini-course held at IMPA in August 2017.
Teaches uncertainty in ML through practical examples.
problem Lack of uncertainty teaching in ML curricula.
method Developed a curriculum and use cases.
result Motivates adoption of uncertainty concepts in AI courses.
An introductory course on hyperbolic geometry for advanced students.
problem None explicitly stated; focuses on teaching hyperbolic geometry.
method Textbook format, intended for advanced undergraduate and early graduate students.
result Teaches advanced students about hyperbolic geometry.
Neural GDEs improve graph prediction by blending discrete structures and differential equations.
problem Dynamic graph prediction challenges in irregularly sampled data.
method Continuous-depth graph neural networks (GNNs) with Neural GDEs.
result Neural GDEs enhance performance across various applications.
New algorithm combines gradient and coordinate descent steps for faster convergence.
problem Optimizing smooth convex functions over atom-spans.
method Blended matching pursuit combining coordinate descent and gradient descent.
result Derives linear convergence rates for non-strongly convex functions.
Blend-ASC improves self-consistency efficiency by dynamically allocating samples, reducing costs.
problem Efficiently applying self-consistency to large datasets is computationally expensive.
method Blend-ASC dynamically allocates samples during inference, improving efficiency.
result Blend-ASC reduces sample usage by 6.8x on average compared to vanilla self-consistency.
Optimization techniques for machine learning explained.
problem Improving machine learning models' performance.
method Course notes and tutorials on optimization methods.
result Comprehensive coverage of optimization techniques.
Notes for a one semester course. The notes contain a description of compact three dimensional Seifert fibered spaces and a classification up to homeomorphism of compact three dimensional Seifert fibered spaces with non-empty boundary.
BiPE blends intra-segment and inter-segment encodings for better length extrapolation.
problem Improving length extrapolation in language models.
method Bilevel Positional Encoding (BiPE) that separates intra-segment and inter-segment encodings.
result BiPE enhances length extrapolation across various text modalities.
These are the lecture notes for an advanced Ph.D. level course I taught in Spring'02 at the C.N. Yang Institute for Theoretical Physics at Stony Brook. The course primarily focused on an introduction to stochastic calculus and derivative pricing with various stochastic computations recast in the language of path integr…
Course on knots using branched coverings.
problem Determining knots using cyclic branched coverings.
method Cyclic branched coverings of knots.
result New insights into knot classification.