Meridian lemma extended to fully alternating links in thickened surfaces.
problem Extending Menasco's meridian lemma to fully alternating links in thickened surfaces.
method Developed a new meridian lemma for fully alternating links in thickened orientable surfaces of positive genus.
result The meridian lemma holds for fully alternating links in thickened surfaces.
We prove hyperbolicity for links in thickened surfaces.
problem Proving hyperbolicity for links in thickened surfaces.
method Defining fully alternating links and proving their hyperbolicity; extending results to essential surfaces.
result Prime, fully alternating links in SimesI are hyperbolic. Study shows surgeries on specific links result in L-spaces.
problem Understanding surgeries on specific links and their outcomes.
method Proving surgeries on chainmail links yield L-spaces using alternating surgeries.
result Alternating surgeries on chainmail links result in total L-spaces.
We propose two new alternating direction methods to solve "fully" nonsmooth constrained convex problems. Our algorithms have the best known worst-case iteration-complexity guarantee under mild assumptions for both the objective residual and feasibility gap. Through theoretical analysis, we show how to update all the al…
New examples show no upper bounds on link volumes on incompressible surfaces.
problem Finding upper bounds on volumes of links on incompressible surfaces.
method Examined weakly generalised alternating and fully augmented links on incompressible surfaces.
result Found infinite families of links on incompressible surfaces with no upper bounds on volume.
DeFi doesn't fully remove trust, showing run risk and personal character's importance.
problem The need for trust in DeFi despite its code-based approach.
method Natural experiment revealing identities of DeFi participants, including a criminal.
result DeFi doesn't fully remove trust, showing run risk and personal character's relevance.
This paper explores approximations for fully Bayesian Gaussian Process Regression.
problem Learning in Gaussian Process models through hyperparameter adaptation.
method Two approximation schemes: Hamiltonian Monte Carlo and Variational Inference.
result Predictive performance analysis on various benchmark datasets.
New communication topologies improve deep reinforcement learning efficiency.
problem Optimizing communication topology for faster and more robust learning in deep reinforcement learning.
method Introduced alternative network topologies (Erdos-Renyi random graphs) and compared their performance with fully-connected and star topologies.
result Erdos-Renyi random graphs outperform fully-connected networks in deep reinforcement learning tasks.
In this paper, we discuss the approaches we took and trade-offs involved in making a paper on a conceptual topic in pattern recognition research fully reproducible. We discuss our definition of reproducibility, the tools used, how the analysis was set up, show some examples of alternative analyses the code enables and …
Chirality affects the curvature of molecular networks, influencing their shape and stability.
problem Understanding how chirality influences the curvature of molecular networks.
method Langevin dynamics simulations and constrained gradient optimization of square lattice networks.
result Linking chirality dictates the sign of Gaussian curvature in molecular chainmail networks.
New methods solve saddle point problems without line search.
problem Solving saddle point problems efficiently and adaptively.
method Auto-conditioned primal-dual hybrid gradient (AC-PDHG) and auto-conditioned ADMM (AC-ADMM) methods.
result Methods achieve optimal complexity and convergence guarantees.
This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one …
We give a short proof of a theorem of Handel and Mosher stating that any finitely generated subgroup of Out(FN) either contains a fully irreducible automorphism, or virtually fixes the conjugacy class of a proper free factor of FN, and we extend their result to non finitely generated subgroups of $\text{Ou…
In \cite{Ka14} we produced an algorithm for deciding whether or not an element φ∈Out(FN) is an iwip ("fully irreducible") automorphism. At several points that algorithm was rather inefficient as it involved some general enumeration procedures as well as running several abstract processes in parallel. In this pape…
New estimators improve sparse semiparametric additive modeling.
problem Sparse semiparametric additive modeling with structured sparsity.
method Combines group subset selection with shrinkage for nonconvex optimization.
result New estimators outperform alternatives in synthetic and real-world data.
We propose a probabilistic numerical algorithm to solve Backward Stochastic Differential Equations (BSDEs) with nonnegative jumps, a class of BSDEs introduced in [9] for representing fully nonlinear HJB equations. In particular, this allows us to numerically solve stochastic control problems with controlled volatility,…
Proposes a new method for MR image reconstruction using unsupervised deep learning.
problem Compensating for missing k-space data in MR images.
method Learns the probability distribution of fully sampled MR images using VAE and uses it as an explicit prior term in reconstruction.
result Produces high-quality reconstructions with low RMSE values, outperforming other methods.
BayGo framework optimizes decentralized learning in multi-agent networks.
problem Information heterogeneity in multi-agent networks.
method Bayesian learning and graph optimization framework with fast convergence.
result Estimation error decreases exponentially with each iteration.
Decentralized learning of personalized models and collaboration graphs without central coordination.
problem Training personalized models and collaboration graphs in a decentralized manner without a central coordinator.
method Alternates between training models given the graph and updating the graph given the models, using peer-to-peer exchanges.
result Communication-efficient approach that avoids exchanging personal data, with benefits demonstrated on synthetic and real datasets.
Proposes a Bayesian Autoencoder with sparse Gaussian process priors to capture data correlations.
problem Autoencoders' i.i.d. assumption of latent representations fails to capture data correlations.
method Imposes fully Bayesian sparse Gaussian Process priors on the latent space of a Bayesian Autoencoder and uses stochastic gradient Hamiltonian Monte Carlo for posterior estimation.
result Consistently outperforms alternatives relying on Variational Autoencoders on various tasks.
Scalable Bayesian inference for state space models using variational inference and SMC.
problem Performing fully Bayesian inference in state space models.
method Combining variational methods with sequential Monte Carlo sampling.
result Fully Bayesian inference of dynamic latent states and static parameters.
The backpropagation algorithm for calculating gradients has been widely used in computation of weights for deep neural networks (DNNs). This method requires derivatives of objective functions and has some difficulties finding appropriate parameters such as learning rate. In this paper, we propose a novel approach for c…
Novel unsupervised random forests improve density estimation and data synthesis.
problem Density estimation and data synthesis for complex tabular data.
method Recursive unsupervised random forests with alternating generation and discrimination rounds.
result Provable consistency and smooth densities with fast execution.
Analog method solves portfolio optimization problems faster and more efficiently.
problem Accurate covariance matrix estimation and fast optimal portfolio selection for financial applications.
method Two-step process using equilibrium propagation and analog Hopfield networks.
result Fully analog pipeline calculates optimal portfolios in energy-efficient manner.
We depart from the usual methods for pricing contracts with the counterparty credit risk found in most of the existing literature. In effect, typically, these models do not account for either systemic effects or at-first-default contagion and postulate that the contract value at default equals either the risk-free valu…
Bayesian neural networks benefit from fully marginalizing over all modes to improve generalization.
problem Bayesian neural networks suffer from multimodal posterior distributions that can lead to suboptimal generalization.
method Use appropriate Bayesian sampling tools to fully marginalize over all posterior modes.
result Training with full marginalization improves the ability of the network to reason between multiple candidate solutions.
Study shows Transformer and Neural GPU are Turing complete without external memory.
problem Exploring computational power of modern neural network architectures.
method Analyzing computational properties of Transformer and Neural GPU.
result Transformer and Neural GPU are Turing complete without external memory.
AGF explains feature learning in neural networks through alternating steps.
problem Understanding what features neural networks learn and how they learn them.
method AGF is an algorithmic framework that approximates the dynamics of feature learning in two-layer networks.
result AGF provides a unified framework to understand feature learning in neural networks, matching experimental results across various architectures.
Decision trees perform well in complex interactions, even when interactions are not fully accounted for.
problem Interpreting complex interactions in machine learning models.
method Experiments on datasets and two methods for robust GLMs.
result Tree depth compensates for model misspecification, enhancing performance in complex scenarios.
We extend kernelized matrix factorization with a fully Bayesian treatment and with an ability to work with multiple side information sources expressed as different kernels. Kernel functions have been introduced to matrix factorization to integrate side information about the rows and columns (e.g., objects and users in …
New algorithm for partially observable contexts in finance.
problem Decision making based on partially observable, correlated market information.
method EMKF-Bandit algorithm integrating system identification, filtering, and bandit algorithms.
result Sub-linear regret under conditions on filtering.
Study shows space writhe closely correlates with knot signature in polymers.
problem Understanding the relationship between space writhe and knot signatures in knotted polymers.
method Performed Langevin dynamics simulations of knotted polymers to measure space writhe.
result Space writhe is strongly correlated with knot signature in complex knots.
Adaptive classification methods ensure correct prediction intervals.
problem Developing methods to ensure correct prediction intervals for classification problems.
method Specialized conformal inference techniques combining cross-validation+, jackknife+, and a novel conformity score.
result The methods provide guaranteed approximate conditional coverage for complex data distributions.
New communication topologies improve deep reinforcement learning performance.
problem Improving performance of learning agents in distributed reinforcement learning.
method Examined four graph families for communication topologies and found Erdos-Renyi random graphs to outperform fully connected topologies.
result Erdos-Renyi random graphs can improve performance of distributed learning agents.
Designs a DNN with alternative-specific utility functions for improved choice analysis.
problem Challenges in reconciling domain-specific knowledge with generic DNN.
method Integrates prior behavioral knowledge into DNN architecture with alternative-specific utility functions.
result 2-3% higher prediction accuracy than fully connected DNN over hyperparameter space.
Alternative neural network training using monotone variational inequality.
problem Training neural networks efficiently and with guarantees.
method Using monotone variational inequality to solve non-convex problems efficiently.
result Our approach leads to fast convergence and competitive performance compared to traditional methods.
Graph neural network simplifies to linear model with attention mechanisms.
problem Semi-supervised learning with limited labeled data.
method Proposes a graph neural network removing fully-connected layers and replacing them with attention mechanisms.
result Attention-based graph neural network outperforms existing methods on benchmark datasets.
Develops deep neural networks for efficient salient object detection.
problem Efficiency and accuracy in detecting salient objects of various scales and semantic information.
method Hybrid contrast-oriented deep neural networks combining fully convolutional and segment-level spatial pooling streams, with an attentional module for fusion.
result Significantly outperforms state-of-the-art methods on six benchmark datasets.
Bayesian optimisation outperforms standard ML-II in small samples.
problem Standard ML-II fails in small-sample trials.
method Adopting fully Bayesian optimisation (FBO) as an alternative.
result FBO is more robust and practical than ML-II.
Bayesian approach improves performance in Gaussian process models.
problem Scalable posterior estimation in Gaussian process models.
method Revisiting variational inference techniques with Bayesian treatment of inducing variables and hyper-parameters.
result State-of-the-art performance demonstrated across various regression and classification problems.
Researchers create new operators from Riemannian invariants.
problem Developing new mathematical tools for Riemannian geometry.
method Introducing formally self-adjoint conformally covariant polydifferential operators.
result Found a fourth-order, conformally covariant tridifferential operator.
This work improves DNN weight quantization with ADMM, achieving lossless binarization and reduced search space.
problem Improving DNN model compression and accuracy with low bit quantization.
method Extending ADMM framework for DNN weight quantization with progressive multi-step approach.
result Achieved lossless and fully binarized DNNs with reduced accuracy loss.
Proposes Robust Matrix Factorization with Grouping Effect (GRMF) for better performance and robustness.
problem Improves matrix factorization by incorporating grouping effect for better performance and robustness.
method Integrates grouping effect into matrix factorization, using an efficient alternating minimization framework with DC programming and ADMM.
result Demonstrates improved performance and robustness compared to five benchmark algorithms on real-world data sets with outliers and noise.
Bayesian deep learning avoids underfitting by projecting onto null space of generalized Gauss-Newton matrix.
problem Bayesian deep learning often underfits, leading to less accurate predictions than point estimates.
method Proposes a matrix-free algorithm to project onto the null space of the generalized Gauss-Newton matrix, ensuring Bayesian predictions do not underfit.
result The method scales to large models, including vision transformers with 28 million parameters, and avoids underfitting.
Subsurface projection has become indispensable in studying the geometry of the mapping class group and the curve complex of a surface. When the subsurface is an annulus, this projection is sometimes called relative twisting. We give two alternate versions of relative twisting for the outer automorphism group of a free …
Bayesian approach improves deep learning performance by concentrating posterior on optimal networks.
problem Improving generalizability of deep neural networks by reducing overfitting and optimizing architecture design.
method Spike-and-Slab Deep Learning (SS-DL) for fully Bayesian regularization of ReLU networks.
result Posterior distribution concentrates at near minimax rate for unknown smoothness levels, matching optimal network size.
Paper proposes a universal probabilistic model for handling instance-dependent label noise.
problem Instance-dependent label noise in data quality challenges DNN training robustness.
method Categorizes instances into confusing and unconfusing, proposes a probabilistic model.
result Significant improvements in robustness over state-of-the-art methods on various datasets.
We demonstrate that the uniqueness of solutions to a broad class of parabolic geometric evolution equations can be proven via a direct and essentially classical energy argument which avoids the DeTurck trick entirely. Previously, we have used a variation of this technique to give an alternative proof and slight extensi…