Optimizes ligand binding poses using CNNs and atomic grids.
problem Improving the accuracy of docking predictions for drug discovery.
method Differentiable atomic grid representation, CNN for scoring and optimization.
result Iteratively-trained CNNs outperform single CNNs in optimizing poses.
New algorithm optimizes pose graphs for SFM and SLAM.
problem Optimizing pose graphs for structure from motion and simultaneous localization and mapping.
method Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm.
result Robust initialization and uncertainty estimates for reliable solutions.
KeypointNet learns 3D keypoints for object pose estimation without ground-truth.
problem Learning 3D keypoints for object pose estimation without manual annotations.
method End-to-end geometric reasoning framework to discover keypoints.
result End-to-end framework outperforms fully supervised baseline.
The paper learns pose variations within shape populations using constrained mixtures of factor analyzers.
problem Learning pose variations within a shape population with articulated parts and relative rotations.
method Formulated as mixtures of factor analyzers, segmentation by component posterior probabilities, and constraints on factor loading matrices for rotation matrices.
result Automatic learning of pose variations from shape populations, resulting in smooth and realistic animations.
Paper optimizes estimation of quadratic functionals in nonparametric IV models.
problem Optimal estimation of a nonlinear functional in ill-posed inverse regression.
method Adaptive, minimax estimation using leave-one-out, sieve NPIV estimator with data-driven sieve dimension selection.
result Adaptive estimator achieves minimax optimal rate in various ill-posed cases.
This paper concerns the numerical solution of the finite-horizon Optimal Investment problem with transaction costs under Potential Utility. The problem is initially posed in terms of an evolutive HJB equation with gradient constraints. In Finite-Horizon Optimal Investment with Transaction Costs: A Parabolic Double Obst…
New framework assesses regularization norms in ill-posed problems, revealing L2 instability and proposing adaptive fractional RKHS solutions.
problem Comparative analysis of regularization norms in ill-posed problems.
method Small noise analysis framework for Tikhonov and RKHS regularizations.
result Optimal convergence rates achieved with adaptive fractional RKHS, but hyper-parameters decay too fast.
This paper improves 3D pose recovery from 2D images using non-convex regularization.
problem 3D object pose recovery from 2D images.
method Proposes non-convex regularization with leaky capped ℓ1-norm (LCNR) and multi-stage optimization.
result Theoretical analysis shows estimation error decreases with optimization stages.
The techniques and analysis presented in this thesis provide new methods to solve optimization problems posed on Riemannian manifolds. These methods are applied to the subspace tracking problem found in adaptive signal processing and adaptive control. A new point of view is offered for the constrained optimization prob…
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding an…
This work models GHG offset credit markets to find optimal strategies for market participants.
problem Optimizing GHG offset credit markets to reduce emissions and penalize excess emissions.
method Characterized optimal behavior in single-player and two-player GHG offset credit markets using optimal stopping and control problems, and mixed-Nash equilibria.
result Market participants benefit from optimal OC trading and generation, highlighting the importance of acting optimally.
The paper explores deep image priors for solving inverse problems.
problem Solving ill-posed inverse problems in image processing.
method Introduces and analyzes deep image priors as optimization of Tikhonov functionals.
result Analytic results for specific network designs and linear operators.
Develops optimal portfolio selection for forward performance in stochastic factor models.
problem Optimal portfolio selection under forward investment performance criteria in an incomplete market.
method Constructs forward performance processes and optimal portfolios by solving non-linear PDEs for stock-factor correlation matrices with EVE structure.
result Establishes explicit forms of generalized Widder's theorem for Laplace inversion in time of solutions to linear PDEs.
We propose a method to generate multiple diverse and valid human pose hypotheses in 3D all consistent with the 2D detection of joints in a monocular RGB image. We use a novel generative model uniform (unbiased) in the space of anatomically plausible 3D poses. Our model is compositional (produces a pose by combining par…
Paper tackles in-bed pressure-based pose estimation, improving accuracy.
problem Pose estimation models fail to generalize with in-bed pressure data.
method End-to-end framework with a deep neural network pre-processing pressure data.
result Model accurately reconstructs unclear body parts for improved pose estimation.
In this article we study a multi-asset version of the Merton investment and consumption problem with proportional transaction costs. In general it is difficult to make analytical progress towards a solution in such problems, but we specialise to a case where transaction costs are zero except for sales and purchases of …
The paper analyzes the observability of relative pose estimation using dual quaternions.
problem Estimating relative pose in robotics applications.
method Lie algebraic nonlinear observability analysis on a dual quaternion system.
result Dual quaternion representation yields an observability matrix with a simple block triangular structure and full rank.
Bayesian design improves experimental optimization.
problem Computational challenges limit BED practical use.
method Recent advances in BED have reduced computational burdens.
result Effective BED design is now feasible.
Novel approach to OT using kernel mean embeddings controls overfitting and achieves dimension-free sample complexity.
problem Consistently estimate optimal transport plan from samples.
method Pose OT as learning kernel mean embedding, employ MMD regularization.
result ε-optimal recovery of transport plan and map with dimension-free sample complexity.
New model resolves signal ambiguities in ill-posed systems.
problem Signal retrieval from indirect measurements with known models.
method Variational generative model that captures signal distribution.
result Retrieves consistent signals with high fidelity.
SOLVAR efficiently analyzes cryo-EM data's structural variability.
problem Analyzing continuous heterogeneity in cryo-EM data.
method Low-rank assumption on covariance matrix for tractable estimation.
result Accurately captures dominant components of structural variability.
SIRL recovers reward function probability distribution from expert actions.
problem Recovering reward functions from expert demonstrations in reinforcement learning.
method Monte Carlo Expectation-Maximization (MCEM) method to estimate reward function probability distribution.
result SIRL provides a robust and transferable solution to the IRL problem.
Generative flows learn distributions on low-dimensional manifolds robustly via Wasserstein proximals.
problem Learning distributions supported on low-dimensional manifolds robustly.
method Combining Wasserstein-1 and Wasserstein-2 proximal operators to formulate well-posed continuous-time generative flows.
result The combination of Wasserstein-1 and Wasserstein-2 proximals ensures the well-posedness of generative flows, leading to unique and robust learning.
Generates realistic person images for re-id, overcoming pose variations.
problem Lack of cross-view paired training data and pose variations in person re-identification.
method Pose-normalization GAN (PN-GAN) for generating images conditioned on pose.
result Synthesized images enable learning invariant features free of pose variations.
This paper formulates and studies a general continuous-time behavioral portfolio selection model under Kahneman and Tversky's (cumulative) prospect theory, featuring S-shaped utility (value) functions and probability distortions. Unlike the conventional expected utility maximization model, such a behavioral model could…
Generative models improve pose transfer between people.
problem Transferring actions from one person to another.
method Used nearest neighbor and generative models (pix2pix) for pose transfer.
result Generative models outperform k-NN in generating corresponding frames and generalizing outside the action set.
Introduces new performance measures using scaled utility functions.
problem Performance measurement in financial contexts.
method Certainty equivalents defined via scaled utility functions, well-posed portfolio optimization problem under generic conditions.
result Link between portfolio dynamics, benchmark process, and utility function choice in the long-run setting.
Paper introduces EnDKF for more accurate pose tracking.
problem Accurate pose tracking with directional uncertainty.
method EnDKF integrates unit-quaternion attitude representation for better directional uncertainty capture.
result Significant reduction in error compared to traditional methods.
New method for adaptive estimation and inference in econometric models without knowing smoothness.
problem Adaptive estimation and inference in ill-posed linear inverse problems with unknown smoothness.
method Discrepancy principle-based framework for adaptive hyperparameter selection.
result Achieves optimal rates in weak and strong metrics for linear functionals.
New models disentangle body pose and appearance for flexible human body analysis.
problem Interpretable latent space for human body analysis.
method Conditional-DGPose and Semi-DGPose models for disentangled pose and appearance.
result Models enable independent manipulation of pose and appearance.
Researchers develop methods to recover agent behavior from sparse data using Gaussian processes.
problem Recovering agent behavior from limited, noisy data in potential mean field games.
method Two Gaussian process-based frameworks: inf-sup formulation and bilevel approach.
result Surrogate MFG models can accurately reproduce observed data, even when prior information is limited.
The paper analyzes reg-SGD for convex problems, proving convergence and quantifying the rate of convergence.
problem Minimizing convex, L-smooth functions in a Hilbert space.
method Regularized stochastic gradient descent with decaying regularization.
result Strong convergence to the minimum-norm solution without boundedness assumptions.
We consider the problem of optimal portfolio selection under forward investment performance criteria in an incomplete market. The dynamics of the prices of the traded assets depend on a pair of stochastic factors, namely, a slow factor (e.g. a macroeconomic indicator) and a fast factor (e.g. stochastic volatility). We …
Proposes a variational approach to shallow neural networks, bypassing optimization.
problem Theoretical understanding and optimization of shallow neural networks.
method Replaces discrete training with a continuum variational surrogate, proving global well-posedness and regularity.
result Optimal parameter density can be obtained by solving a single linear system, achieving O(1/N) generalization error. CSI-Net learns WiFi signals for body characterization and pose recognition.
problem Unified learning of body characteristics and pose recognition.
method Unified Deep Neural Network (DNN) for WiFi signal representation and multi-task learning.
result CSI-Net solves biometrics estimation and person recognition.
Unsupervised mesh disentanglement separates identity and pose.
problem Geometric disentanglement for 3D deformable models.
method CFAN-VAE architecture using conformal factor and normal features.
result CFAN-VAE achieves state-of-the-art performance on unsupervised geometric disentanglement.
Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning because it automatically extracts meaningful features through a sparse and part-based representation. However, NMF has the drawback of being highly ill-posed, that is, there typically exist many different but equivalent facto…
New CNN architecture improves pediatric image segmentation by homogenizing pose and size.
problem Challenges in segmenting pediatric images due to pose and size heterogeneity.
method Spatial Transformer Network (STN) for pose and scale invariance, combined with UNet for segmentation.
result Improved pediatric segmentation, especially renal tumor delineation, with accelerated processing.
Aerial robot estimates human pose and path using dynamic classifier selection.
problem Estimating human pose and trajectory from aerial video.
method Dynamic classifier selection architecture; perspective correction; HOG and CNN features; 64 pose-viewpoint classes.
result Dynamic classifier selection improves efficiency and accuracy.
Investor optimizes stock investments with noisy future price signals.
problem Optimizing stock investments with uncertain future stock prices.
method Dynamic investment strategy with partial observation of Brownian motion.
result Closed-form solution for optimal investment problem.
In this paper, we deal with two challenges for measuring the similarity of the subject identities in practical video-based face recognition - the variation of the head pose in uncontrolled environments and the computational expense of processing videos. Since the frame-wise feature mean is unable to characterize the po…
Study on utility maximization with Tsallis entropy in reinforcement learning.
problem Exploring utility maximization with Tsallis entropy in reinforcement learning.
method Introducing Tsallis entropy regularizer to induce exploration, investigating specific examples, characterizing well-posedness, designing reinforcement learning algorithm.
result Characterized well-posedness and provided semi-closed-form solutions for specific examples, found distinct optimal strategies.
Geometric Capsule Autoencoders group 3D points into parts and objects.
problem Learning object representations from 3D point clouds.
method Geometric capsules with pose and feature components, Multi-View Agreement voting mechanism.
result Learned representations enable object identification and canonical pose recovery.
Paper generates high-resolution fashion images based on body pose.
problem Limited variety of outfit images for shopping.
method Generative model trained on body pose and style transfer.
result Realistic high-resolution images of models in custom outfits.
Capsule models enforce object pose relationships for robustness, explored with probabilistic generative and variational methods.
problem Enforcing object pose relationships for robustness to viewpoint changes.
method Probabilistic generative model with variational bound, exploring capsule assumptions and inference mechanisms.
result Unified objective and test time optimisation demonstrated for capsule models.
Study geodesics in Heisenberg groups with sub-Finsler metrics.
problem Characterize infinite geodesics in Heisenberg groups.
method Optimal control theory applied to sub-Finsler metrics.
result Proves infinite geodesics are horizontal lines under specific conditions.
We address the problem of portfolio optimization under the simplest coherent risk measure, i.e. the expected shortfall. As it is well known, one can map this problem into a linear programming setting. For some values of the external parameters, when the available time series is too short, the portfolio optimization is …
Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to ill-posed inverse problems. In this letter, we present a data-driven scheme for learning optimal thresholding functions for ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers of a simpl…