New method improves Frank-Wolfe for low-rank matrix completion.
problem Low-rank matrix completion problem.
method Extended Frank-Wolfe method with in-face directions.
result Significant speed-ups in computing very low-rank solutions.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,341 papers · 148 categories
Trend · papers per month
48 results for “in-face directions”
New Frank-Wolfe method for sparse neural networks.
problem Training sparse neural networks.
method Combines Frank-Wolfe steps and steepest descent steps, with in-face directions and block coordinate steps.
result Significant improvements in training sparse neural networks.
This thesis uses deep learning to automatically recognize semantic features in faces.
problem Challenges in machines interpreting semantic information from faces.
method Deep Learning approach to learn semantic features without manual design of feature detectors.
result Demonstrates the effectiveness of deep neural networks in recognizing various semantic features in faces.
Contrastive examples improve fairness in face recognition by balancing minority and majority groups.
problem Face recognition algorithms favor majority groups in training data.
method Create contrastive examples by swapping group memberships in the training dataset.
result Contrastive examples improve fairness metrics like equalized odds.
Robust tensor ring completion improves tensor recovery accuracy and efficiency.
problem Tensor completion sensitivity to sparse components.
method Robust Tensor Ring Completion (RTRC) with weighted nuclear norms and l1 regularization.
result Exact recovery guarantees and superior performance in various tasks.
The study improves the assessment of fairness in face recognition using ROC curves and statistical guarantees.
problem Improving the assessment of fairness in face recognition systems.
method Proves asymptotic guarantees for empirical ROC curves and fairness metrics, and introduces a recentering technique to avoid bootstrap pitfalls.
result Demonstrates the practical relevance of the methods for assessing fairness in face recognition systems.
Novel technique detects adversarial samples in face recognition models.
problem Widespread adversarial sample attacks on DNN models.
method Bi-directional correspondence inference between attributes and internal neurons to identify critical neurons.
result 94% detection accuracy for 7 different kinds of attacks with 9.91% false positives.
CuMPerLay vectorizes CMP for deep learning, improving image analysis.
problem Complex multifiltration structures hinder using CMP in deep learning.
method Introduces a new algorithm for vectorizing MP homologies of cubical complexes.
result Differentiable vectorization enables robust topological feature vectors for deep learning.
Paper introduces a new algorithmic framework for model-based RL with theoretical guarantees.
problem Limited theoretical understanding of model-based RL methods.
method Develops a meta-algorithm that iteratively builds a lower bound of expected reward and maximizes it over policy and model.
result Meta-algorithm achieves state-of-the-art performance with minimal samples.
In this paper we analyze Gresham's Law, in particular, how the rate of inflow or outflow of currencies is affected by the demand elasticity of arbitrage and the difference in face value ratios inside and outside of a country under a bimetallic system. We find that these equations are very similar to those used to descr…
Extends decision theory to handle unlikely events and varying risk attitudes.
problem Handling preferences for unlikely events and varying risk attitudes.
method Derives a subjective lexicographic expected utility representation.
result Provides foundations for models in finance and infinite dynamic games.
New framework for robust uncertainty quantification in strategic settings.
problem Machine learning model predictions can be strategically altered by informed agents.
method Strategic Conformal Prediction framework
result Theoretical guarantees and experimental validation show remarkable effectiveness.
This study analyzes bias in face generation models and proposes mitigation techniques.
problem Bias in face generation models that may misrepresent certain social groups.
method Three approaches: generators, attribute modifier, and post-processing bias mitigators.
result Generators suffer from bias across all social groups, while post-processing mitigators show different feature concentration.
Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradationsstat.ML
Study examines strengths and weaknesses of deep learning models in face recognition.
problem Improving deep learning models for face recognition under degraded conditions.
method Experimentation on four deep CNN models using LFW dataset, investigating effects of image quality and model characteristics.
result Noise, blur, and missing pixels negatively impact face verification performance; contrast changes and compression artifacts have limited effect.
Facial Keypoints Detectionstat.ML
Paper studies facial keypoint detection using various algorithms.
problem Challenges in accurately detecting facial keypoints from complex images.
method Preprocess data with PCA and LBP, apply multiple algorithms including linear regression, tree models, neural networks, and CNNs.
result Demonstrates the effectiveness of the proposed framework through comprehensive experiments.
Face recognition system trained with noisy labels.
problem Label noise in training deep learning classifiers.
method Review and apply recent methods to manage noisy annotations.
result Improved performance of face recognition system with noisy labels.
Enhances generative models with classifier representations.
problem Improving the quality of generative models.
method Discriminative regularization applied to variational autoencoders.
result Generated samples are clearer and have higher visual quality.
In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or in face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regressio…
Uses simulations to detect AI bias in face detection.
problem Bias in machine learning models can lead to poor performance on minority groups.
method Bayesian parameter search to identify weaknesses in ML classifiers.
result Identifies demographic biases in commercial face APIs.
A Coxeter -orbifold is an -dimensional orbifold based on a polytope with silvered boundary facets. Each pair of adjacent facets meet on a ridge of some order , whose neighborhood is locally modeled on modulo the dihedral group of order generated by two reflections. For , we study…
UR-FUNNY dataset aids in understanding multimodal humor.
problem Understanding humor in a multimodal context is understudied.
method Developed a multimodal dataset (UR-FUNNY) for humor detection.
result UR-FUNNY opens the door to multimodal humor detection research.
Nonnegative matrix factorization (NMF) with group sparsity constraints is formulated as a probabilistic graphical model and, assuming some observed data have been generated by the model, a feasible variational Bayesian algorithm is derived for learning model parameters. When used in a supervised learning scenario, NMF …
Probabilistic 2DCCA improves face recognition with superior loading factor estimation.
problem Lack of probabilistic interpretation in 2DCCA.
method Probabilistic 2DCCA with an EM-based iterative algorithm.
result Superior performance in loading factor estimation and robustness in face recognition tasks.
This paper constructs new Einstein metrics from old ones using specific deformation factors.
problem Creating new Einstein metrics from existing ones.
method Using a given Einstein metric and its Killing 1-form, determine deformation factors to form a new Einstein metric.
result The new Einstein metric is constructed by applying specific deformation factors to the given metric.
Unsupervised method discovers object landmarks by factorizing image deformations.
problem Learning object structure in unsupervised settings.
method Factorizing image deformations to learn landmarks consistently across different viewpoints and object deformations.
result Learned landmarks establish meaningful correspondences between different object instances without explicit requirement.
Thompson Sampling fails in LQ control problems, resulting in higher regret.
problem Exploration-exploitation tradeoff in linear quadratic control problems.
method Analysis of Thompson Sampling in frequentist setting for LQ control.
result Thompson Sampling results in regret, worse than .
New algorithm learns FMDP structure while minimizing regret.
problem Regret minimization in FMDPs with unknown structure.
method Optimism in face of uncertainty principle combined with statistical structure learning.
result First algorithm to learn FMDP structure while minimizing regret.
Optimizes trading large volumes of volatile assets with fast mean-reverting volatility.
problem Challenges of executing large volumes of illiquid or volatile assets.
method Modeling uncertain volatility and liquidity with fast mean-reverting dynamics, using singular perturbation arguments and high-frequency data.
result Approximately optimal trade execution strategies under fast mean-reversion.
Bayesian Neural Networks improve uncertainty modeling in facial emotion recognition.
problem High aleatoric uncertainty and visual ambiguity in facial emotion recognition.
method Bayesian Neural Networks approximated using MC-Dropout, MC-DropConnect, or Ensemble methods.
result Bayesian Neural Networks produce more human-like output probabilities.
Encoding Curved Tetrahedra in Face Holonomies: a Phase Space of Shapes from Group-Valued Moment Mapsmath-ph
We present a generalization of Minkowski's classic theorem on the reconstruction of tetrahedra from algebraic data to homogeneously curved spaces. Euclidean notions such as the normal vector to a face are replaced by Levi-Civita holonomies around each of the tetrahedron's faces. This allows the reconstruction of both s…
Universal adversarial patches prevent face detection in various frameworks.
problem Preventing face detection in state-of-the-art face detection systems.
method Investigated the phenomenon of patches that suppress face detection and proposed optimization-based approaches for automatic design.
result Universal adversarial patches can prevent face detection without introducing false positives.
Face verification remains a challenging problem in very complex conditions with large variations such as pose, illumination, expression, and occlusions. This problem is exacerbated when we rely unrealistically on a single training data source, which is often insufficient to cover the intrinsically complex face variatio…
New method generates unrestricted adversarial face images to bypass robust face recognition systems.
problem Vulnerability of face recognition systems to unrestricted adversarial examples.
method Image translation techniques to generate large perturbations in face images.
result Achieved high attack success rates ( and ) under white- and black-box settings.
One basic requirement of many studies is the necessity of classifying data. Clustering is a proposed method for summarizing networks. Clustering methods can be divided into two categories named model-based approaches and algorithmic approaches. Since the most of clustering methods depend on their input parameters, it i…
Paper proposes LMM-PQS for cross-domain few-shot learning.
problem Cross-domain few-shot learning problem.
method Generates pseudo query images and fine-tunes feature extraction modules with a large margin mechanism.
result LMM-PQS outperforms baseline models in cross-domain few-shot learning.
Paper proposes SiSTA for single-shot domain adaptation using target-aware generative augmentation.
problem Adapting models from source to target domains with limited target data.
method Fine-tunes a generative model on a single-shot target and uses novel sampling strategies for synthetic data.
result Improves performance by up to 20% over existing baselines in face attribute detection.
Paper develops security model and pricing for stable digital currency in quantum blockchain network.
problem Securing and pricing stable digital currency in a quantum blockchain network.
method Developed a block-based quantum channel networking technology and a FinTech platform model with dynamic pricing.
result Established a generalized IoB security model using quantum channel networking and QKD.
Optimal algorithm for two-player zero-sum games with linear parameterization.
problem Finding Nash Equilibrium in two-player zero-sum Markov games with linear transition.
method Nash-UCRL algorithm, Coarse Correlated Equilibrium, Optimism-in-Face-of-Uncertainty.
result Proves regret bound, matching lower bound up to logarithmic factors.
Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionally-trained finite state machines. A key advantage of these models is their great flexibility to include a wide array of overlapping, multi-granularity, non-independent features of the input. In face of thi…
UCBMQ improves Q-learning by adding momentum to correct bias and limit regret.
problem Improving Q-learning's bias and regret in reinforcement learning.
method UCBMQ combines Q-learning with an upper confidence bound and momentum term.
result UCBMQ guarantees a regret of with a linear second-order term in .
The paper tackles conditional multimodal learning using variational methods.
problem Learning conditional distributions between modalities.
method Variational methods for maximizing conditional log-likelihood.
result Generated faces are more representative of the attributes.
MaxVA improves Adam's step sizes by maximizing gradient variance.
problem Unstable or extreme adaptive learning rates in Adam.
method Maximizing the variance of gradient coordinates in Adam's running mean of squared gradients.
result Faster adaptation and more desirable convergence behaviors than Adam.
Paper shows affine constraint is unnecessary for high-dimensional data.
problem The necessity of an affine constraint in affine subspace clustering.
method Theoretical and empirical analysis of conditions for correctness of affine subspace clustering methods.
result Affine constraint has negligible effect on clustering performance for high-dimensional data.
Agents collaboratively learn optimal policies in MDPs with limited capabilities.
problem Learning optimal policies in MDPs with heterogeneous agents and limited communication.
method Introduced concepts of leakage probabilities and proposed Federated-Q protocol (FedQ) for collaborative learning.
result FedQ protocol effectively aggregates knowledge and modifies learning problems for further training.
Bayesian optimization generates personalized face stimuli for cognitive neuroscience.
problem Lack of personalized face stimuli in cognitive neuroscience studies.
method Combines GANs with Bayesian optimization to identify individual response patterns to faces.
result Algorithm efficiently generates optimal faces maximizing individual subject's response.
New method improves reliability of recommender systems in face of fake accounts and manipulation.
problem Improving reliability of recommender systems in the presence of fake accounts and manipulation.
method Robust Discrete Matrix Completion (RDMC) method designed to handle sparse rating data and manipulation.
result Evaluations show RDMC offers a statistically-sound blueprint for future studies on recommender systems.
An adversarial learning framework for preserving users' anonymity in face-based emotion recognitioncs.LG
Paper proposes a framework to protect user anonymity in emotion recognition.
problem Preserving user anonymity in face-based emotion recognition systems.
method Adversarial learning framework using CNN architecture.
result The proposed approach minimizes identity-specific information and maximizes emotion-dependent information.
Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learningcs.LG
Algorithm optimizes constrained reinforcement learning with dual variables.
problem Minimizing convex functional subject to convex constraint in large state spaces.
method VPDPO algorithm using Lagrangian and Fenchel duality.
result Achieves sublinear regret and constraint violation, globally optimal policy.