Paper introduces 'plausible deniability' for privacy-preserving data synthesis.
problem Challenges in releasing full data records while preserving privacy.
method Introduces 'plausible deniability' criterion and mechanisms for generating synthetic datasets.
result Generative technique preserves utility of original data and is efficient for large datasets.
Privacy-preserving inference for clinical trials using differential privacy.
problem Balancing knowledge sharing and privacy in healthcare data.
method Differential privacy (DP) applied to log-linear belief updates in distributed settings.
result Differentially private, distributed inference methods outperform existing techniques.
The paper presents a method to preserve privacy in text analysis using calibrated noise.
problem Accurately learning from user data while maintaining privacy.
method Calibrated multivariate perturbations applied to word embeddings to achieve geo-indistinguishability.
result The method provides better privacy guarantees than baseline models with minimal utility loss.
New method protects whistleblowers from retaliation by ensuring their reports remain private.
problem Whistleblowers face retaliation, and current protections are insufficient.
method Formalizes protection against strong-adversary threat model as per-report (0,δ)-differential privacy, and provides a generic mechanism to reduce private auditing to private continual counting. result Demonstrates a reduction in selection error and improved utility over randomized response.
Local differential privacy method protects image privacy in machine learning.
problem Protecting image privacy in machine learning models from data owners.
method Locally perturbs image representations before releasing them to the data user.
result Proposes DCAConv, an image feature extractor that maintains high data utility while preserving privacy.
Secure submodel learning protects privacy in federated learning.
problem Efficiency and privacy in federated learning for resource-constrained clients.
method Designing a secure federated submodel learning scheme with randomized response, secure aggregation, and Bloom filter.
result Demonstrated the feasibility and scalability of the scheme with practical evaluations.
ARFs generate plausible counterfactuals for models, improving model understanding.
problem Creating realistic counterfactuals for model analysis.
method Adversarial Random Forests (ARFs) for generating plausible counterfactuals.
result ARFs efficiently generate plausible counterfactuals in a model-agnostic way.
We define and compute plausible counterfactual explanations using density constraints.
problem Efficiently compute plausible counterfactual explanations for machine learning models.
method Propose and study a formal definition of plausible counterfactual explanations, use density estimators, and introduce convex density constraints.
result Convex density constraints ensure plausible and feasible counterfactual explanations.
New method generates plausible counterfactuals for time series classification.
problem Generating realistic counterfactuals for time series data.
method Gradient-based optimization with soft-DTW alignment and multi-faceted loss function.
result Our method outperforms existing approaches in temporal realism and distributional alignment.
OFVF uses Bayesian plausibility sets for robust exploration in reinforcement learning.
problem Exploration in reinforcement learning, especially in poorly understood states and actions.
method Bayesian approach with plausibility sets constructed based on sensible value functions.
result OFVF constructs tighter optimistic estimates for exploration, leading to robust policies.
Paper explores BP's biological plausibility and alternative learning algorithms.
problem Backpropagation's biological plausibility in neural networks.
method Framing supervised learning in the Lagrangian framework to devise biologically plausible local algorithms.
result Biologically plausible learning algorithms can be devised based on saddle point search in the learning adjoint space.
Method generates plausible financial stress scenarios using large deviations.
problem Misleading risk management by overlooking or overemphasizing implausible scenarios.
method Exploits large-deviations principle to concentrate risk factors near most likely stress configurations.
result Can generate informative stress scenarios even with limited historical data.
AR algorithm simplifies backpropagation with improved scalability and biological plausibility.
problem Improving backpropagation algorithms for complex neural networks and biological plausibility.
method Introducing learnable backwards weights and avoiding nonlinear derivative computations; relaxing frozen feedforward pass assumption.
result Simplified AR algorithm maintains performance on complex CNN architectures and challenging datasets.
Generative model learns distribution over plausible segmentations.
problem Learning from ambiguous images in clinical applications.
method Probabilistic U-Net combining U-Net and conditional variational autoencoder.
result Significantly better at reproducing plausible segmentations than previous methods.
Derives a biologically plausible neural network for Slow Feature Analysis.
problem Learning latent features from time series data.
method Starting from an SFA objective, derives Bio-SFA with a biologically plausible neural network implementation.
result Validates Bio-SFA on naturalistic stimuli, reproducing interesting properties of brain cells.
New neural network theory mimics physics laws, making computations more plausible.
problem Neural networks lack biological plausibility in computations.
method Variational framework of the least action principle, local in space and time.
result SpatioTemporal Local Propagation (STLP) scheme is biologically plausible.
New framework predicts diverse, contextually plausible 3D human motions.
problem Predicting multiple plausible future 3D poses given observed poses.
method Developed a new variational framework that conditions latent variable on past observation to encourage relevant information.
result Our approach generates motions of higher quality and preserves contextual information.
GAIT-prop derives a biologically plausible learning rule from backpropagation.
problem Biological implausibility in traditional backpropagation for neural networks.
method GAIT-prop uses a top-down model to convert output error into plausible targets for weight updates.
result GAIT-prop and backpropagation give identical weight updates under certain conditions.
Shallow networks with local learning rules can match deep learning performance.
problem Training deep neural networks is biologically implausible; the goal is to achieve similar performance with shallow networks.
method Investigated shallow networks with one hidden layer and a single readout layer, using various local learning rules for the hidden layer and supervised learning for the readout layer.
result Shallow networks can achieve test accuracy comparable to deep learning models, suggesting the use of different datasets for testing.
Biologically plausible learning algorithms can match BP on large datasets.
problem Learning algorithms that are biologically plausible often perform poorly on large datasets.
method Evaluation of sign-symmetry and feedback alignment algorithms on ImageNet and MS COCO.
result Sign-symmetry algorithm can match BP performance on ImageNet and MS COCO.
Backpropagation is explained as a diffusion process in neural networks.
problem The biological plausibility of Backpropagation is questioned.
method Demonstrated that time-delayed neurons and forward-backward waves approximate the gradient in deep networks.
result Backpropagation can be interpreted as a diffusion process, approximating the gradient for non-fast inputs.
New learning rules from information bottleneck improve deep learning without precise labels.
problem Training deep neural networks with backpropagation is biologically implausible.
method Kernelized information bottleneck principle with 3-factor Hebbian structure.
result The new learning rules perform nearly as well as backpropagation on image classification tasks.
New measures for prediction validity and consonant plausibility introduced.
problem Challenges in predicting future observations and quantifying prediction uncertainty.
method Introducing Type-2 validity and using consonant plausibility measures and conformal prediction.
result Achieving both Type-1 and Type-2 validity through consonant plausibility measures and conformal prediction.
Two approaches to model selection in networks yield similar results, but not always.
problem Determining which model best describes a network while avoiding overfitting.
method Comparing model selection based on posterior probability and predictive performance.
result The most plausible model is not always the most predictive, leading to potential overfitting.
New method isolates epistemic uncertainty in diffusion models, improving plausibility scores.
problem Uncertainty quantification in diffusion models, especially epistemic uncertainty.
method Fisher information based approach using FLARE (Fisher-Laplace Randomized Estimator).
result FLARE improves uncertainty estimation in synthetic time-series generation tasks.
New algorithm shows neural networks can learn without full backpropagation.
problem Stochastic gradient descent with backpropagation is non-biologically plausible.
method Random and fixed backpropagation weights in a feedback alignment algorithm.
result Error converges to zero exponentially fast in overparameterized networks.
Proposes a new neural network approach to credit assignment.
problem Credit assignment problem in deep neural networks.
method Contrastive similarity matching objective function.
result Deep networks learn to match similarity between layers.
This note improves correlation stress tests using geodesic distance.
problem Improving financial risk management through better covariance stress tests.
method Proposes a new geometrically invariant definition of correlation stress tests.
result Demonstrates a submanifold approach to stress testing covariance matrices.
We propose to interpret distribution model risk as sensitivity of expected loss to changes in the risk factor distribution, and to measure the distribution model risk of a portfolio by the maximum expected loss over a set of plausible distributions defined in terms of some divergence from an estimated distribution. The…
One conjecture in both deep learning and classical connectionist viewpoint is that the biological brain implements certain kinds of deep networks as its back-end. However, to our knowledge, a detailed correspondence has not yet been set up, which is important if we want to bridge between neuroscience and machine learni…
New learning algorithm mimics biological neural networks.
problem Biologically implausible backpropagation for directed neural networks.
method Introduces new neuronal dynamics and learning rule for arbitrary architectures, sparsity-inducing pruning method, and dynamical-systems characterization.
result Prunes irrelevant connections and improves learning efficiency.
New method uses entropy to generate multiple plausible causal maps.
problem Learning causal relationships from noisy data can lead to artifacts in DAGs.
method Entropy-based inference to generate an ensemble of plausible causal graphs.
result Multiple causal maps consistent with underlying data variability.
Stock markets show unusual overnight and intraday returns.
problem Unusual patterns of overnight and intraday returns in stock markets.
method Analyzed features of the returns to deduce the cause.
result The only plausible explanation for these returns is that they are due to market manipulation.
We propose a method to model multi-agent behaviors with limited observation and mechanical constraints.
problem Modeling real-world multi-agent behaviors with limited observation and mechanical constraints.
method Decentralized generative models with partial observation and mechanical constraints based on hierarchical variational recurrent neural networks.
result Our method effectively models and predicts biologically plausible behaviors with minimal constraint violations.
Unsupervised learning by hidden units with biological plausibility.
problem Training neural networks without labeled data.
method Global inhibition in hidden layer to learn feature detectors.
result Learned feature detectors enable supervised training of higher layers.
New method trains neural networks with local error signals, outperforming global methods.
problem Training neural networks with global error signals.
method Layer-wise training with local error signals.
result Layer-wise training with local error signals can approach state-of-the-art performance.
The paper relaxes constraints on predictive coding models, making them more biologically plausible.
problem Neurophysiological models of predictive coding are not fully biologically plausible.
method The paper relaxes constraints on standard predictive coding algorithms by removing neurally implausible features.
result The removal of neurally implausible features does not significantly affect learning performance.
This work combines RBMs with neural nets for reliable source and statement estimation.
problem Discovering the truth from conflicting or scarce claims.
method Combines RBMs with neural nets for unsupervised inference.
result Significantly outperforms state-of-the-art methods.
Neural network models of early sensory processing typically reduce the dimensionality of streaming input data. Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules. When derived from a principled cost…
Biological neural network mimics CCA for multi-channel data.
problem Implementing CCA in a biologically plausible neural network.
method Derive an online CCA algorithm with local synaptic updates for multi-compartmental neurons.
result The derived neural network architecture and synaptic updates resemble cortical pyramidal neuron behavior.
Proposes VPF for efficient training of DBMs without Gibbs sampling or feedback phases.
problem Efficient training of deep neural networks with biological plausibility.
method Variational Probability Flow (VPF) for binary Deep Boltzmann Machines (DBMs).
result VPF learns features quickly and generates high-likelihood samples.
Probabilistic inpainting learns multiple plausible images from missing data.
problem Generating multiple plausible images from missing data in images.
method Building a PixelCNN model that learns a distribution of images conditioned on visible pixels.
result The method produces diverse and realistic inpaintings.
We present some plausible definitions for the tangent grupoid of a manifold M, as well as some of the known applications of the structure. This is a kind of introductory note.
AGATHA predicts promising research directions from scientific data.
problem Efficiently winnowing thousands of potential targets in drug discovery.
method Deep-learning hypothesis generation system that ranks plausible term-pairs.
result Best-in-class performance on an established benchmark, high recommendation scores across subdomains.
A new method for target propagation using iterative approximations converges fast and is more biologically plausible.
problem Improving target propagation methods for neural networks.
method Iterative approximate inverses and local auto-encoders.
result The method converges exponentially fast under certain conditions.
Spiking neural networks perform similarly to deep networks on occluded images.
problem Robust object recognition in partially occluded images.
method Developed a two-layer spiking neural network trained on natural scenes with a biologically plausible learning rule, compared to deep convolutional networks.
result Spiking neural networks achieve good accuracy and robustness on stepwise pixel erasement tasks.
Fairness GAN generates fair datasets for decision making.
problem Creating fair datasets for allocative decision making.
method Novel auxiliary classifier GAN aiming for demographic parity or equality of opportunity.
result Improves demographic parity and equality of opportunity in generated images.
Survey of integrating physics knowledge into machine learning models.
problem Mitigating data shortage and ensuring physical plausibility.
method Combining physics knowledge with machine learning models.
result Summarizes recent works in physics-informed machine learning.