Pantypes improve prototypical models by capturing diverse input distributions.
problem Prototypical models lack sufficient data representation in low density regions.
method Introducing pantypes, a sparse set of diverse objects to represent the full diversity of input distribution.
result Pantypes empower prototypical models to foster high diversity, interpretability, and fairness.
This work generates diverse adversarial attacks for different domains using latent variable perturbation.
problem Adversarial attacks on deep neural networks are limited to a single perturbation.
method Frame adversarial attacks as learning a distribution of perturbations, enabling generation of diverse attacks.
result Framework generates competitive or superior adversarial attacks across diverse domains (images, text, graphs).
MOD improves ensemble-based uncertainty estimates by encouraging larger diversity.
problem Improving model uncertainty estimates for inputs not seen during training.
method Maximize Overall Diversity (MOD) approach to encourage larger diversity in ensemble predictions.
result Significantly improves predictive performance for out-of-distribution test examples.
δ-CLUE generates diverse explanations for model uncertainty.
problem Lack of constraints in generating explanations for uncertainty estimates.
method Augmenting CLUE approach to provide a set of plausible explanations.
result Returns a set of diverse inputs that yield confident predictions.
Paper presents a defense framework against adversarial examples.
problem Vulnerability of deep neural networks to adversarial examples.
method Cross-layer strategic ensemble defense with input and output transformations.
result Strategic ensemble defense achieves high defense success rates and robustness.
New methods improve uncertainty explanations for models.
problem Improving interpretation of uncertainty estimates from probabilistic models.
method Developed new methods to generate diverse and global explanations for uncertain model predictions.
result Generated diverse and global explanations for uncertain model predictions, addressing previous limitations.
FoRDE uses input gradients to improve neural network ensembles.
problem Improving neural network ensembles for robustness and accuracy.
method Proposes FoRDE, an ensemble learning method based on ParVI, which repels function space by input gradients.
result FoRDE significantly outperforms DEs and other ensemble methods in accuracy and calibration.
XEnsemble improves DNN robustness against adversarial and out-of-distribution inputs.
problem Protecting DNN models from adversarial and out-of-distribution inputs.
method Diverse input denoising verifiers and disagreement-diversity ensemble learning.
result XEnsemble achieves high defense and detection success rates.
Graph Metanetworks process diverse neural architectures efficiently.
problem Processing diverse neural architectures efficiently.
method Builds metanetworks using graph neural networks to process graphs representing input neural networks.
result Proves GMNs are expressive and equivariant to parameter permutation symmetries.
Enhances adversarial examples' transferability through diverse input patterns.
problem Low success rates of adversarial attacks in the black-box setting.
method Applying random transformations to input images at each iteration.
result Generates adversarial examples that transfer better to different networks.
MODEF combines denoising and verification to defend against adversarial attacks.
problem Vulnerability of deep neural networks to adversarial inputs.
method Cross-layer model diversity ensemble combining unsupervised denoising and supervised verification.
result MODEF achieves remarkable defense success rates against black-box attacks.
New model evaluates how well models handle input faults.
problem Fault tolerance of models to input variations.
method Evaluates fault tolerance using information-based characteristic for arbitrary valid inputs.
result Proposes a new way to measure model robustness.
Improved RL for grasping in cluttered scenes using state representation learning.
problem Poor performance of RL methods in grasping diverse objects from raw images.
method Employed state representation learning (SRL) with disentanglement of raw input images.
result Deep RL can learn grasping skills from varied visual inputs.
DEN learns diverse tasks to generalize to unseen tasks.
problem Generalization from a diverse set of classification tasks with limited data.
method Three-block architecture: covariate transformation, distribution embedding, and classification.
result DEN outperforms existing methods in various synthetic and real tasks.
GLAMOUR learns from macromolecules, overcoming diversity challenges.
problem Challenges in machine learning with macromolecules due to their vast diversity.
method Developed GLAMOUR, a framework for chemistry-informed graph representation of macromolecules.
result Quantifies structural similarity and enables supervised learning for macromolecules.
Neural networks generate their own weights using hypernetworks.
problem Generating diverse and non-trivial weights for neural networks.
method Formulate a compromise between accuracy and diversity, using multi-layered perceptrons for mapping.
result Generated weights are diverse and lie on a non-trivial manifold.
Novel Bayesian neural network method for robustness.
problem Adversarial robustness without online training.
method Distributes uncertainty across all inputs.
result Demonstrates robustness on benchmark datasets.
Model learns diverse molecular transformations for optimization.
problem Optimizing molecules in various ways.
method Junction tree encoder-decoder with adversarial training.
result Model outperforms previous methods on molecular optimization tasks.
Paper introduces a novel method to generate diverse inputs for neural programming by example.
problem Synthesizing programs from input/output pairs using machine learning.
method Uses an SMT solver to generate diverse input-output pairs.
result Generated inputs improve model performance and generalization.
DiwE uses regional distribution changes to create diverse ensemble classifiers for concept drift.
problem Handling concept drift in evolving data streams.
method DiwE measures diversity based on regional distribution disagreement and uses it to weight instances and select classifiers.
result DiwE outperforms other algorithms on various synthetic and real-world data stream benchmarks.
A new method finds diverse near-optimal portfolios using quality-diversity.
problem Optimizing financial portfolios with robustness to input parameter uncertainties.
method Quality-Diversity (QD) optimization using CVT-MAP-Elites algorithm.
result Diverse set of near-optimal portfolios identified.
The paper tackles fair and diverse data summarization using DPPs.
problem Tackles bias in data summarization methods.
method Uses determinantal measures of diversity and corresponding distributions (DPPs) to incorporate fairness constraints.
result Developed a fast sampler for constrained determinantal distributions that is provably good under certain conditions.
Proposes a method to generate diverse outputs in conditional GANs.
problem Mode-collapse in conditional GANs, where outputs are overly simplified.
method Explicit regularization to produce diverse outputs based on latent codes.
result Demonstrates improved diversity in image-to-image translation, inpainting, and future video prediction tasks.
Proposes models to generate more interesting story endings.
problem Generating diverse and interesting story endings for a given context.
method Trains models to focus on keyphrases and promotes non-generic words.
result Models generate more diverse and interesting story endings.
Proposes a method to generate diverse translations by conditioning on target domain.
problem NMT models lack diversity in translations, even with search algorithms.
method Condition the decoder on a latent variable representing target domain, generated by a target encoder.
result Generated diverse translations without affecting performance or training time.
The paper clusters hypergraphs to find diverse and experienced groups based on past experiences.
problem Finding diverse and experienced groups with respect to past experiences.
method Regularized edge-based hypergraph clustering objective with a 2-approximation algorithm.
result Demonstrates an efficient 2-approximation algorithm for clustering hypergraphs.
The paper tackles ambiguous image-to-image translation by modeling a distribution of possible outputs.
problem Ambiguity in image-to-image translation where a single input can have multiple possible outputs.
method The approach involves a conditional generative model that learns to map input images to a latent vector, which is then used to generate diverse outputs.
result The method produces more diverse and realistic outputs compared to other variants.
The paper teaches a reinforcement learning agent to generate diverse programs based on symbolic instructions.
problem Learning to generate diverse programs for diverse scenes given a symbolic instruction.
method Instruction-conditioned reinforced adversarial learning.
result The agent's stochastic policy more accurately captures the diversity in the goal distribution.
Proposes Vendi Score for evaluating diversity in ML models.
problem Lack of flexible diversity evaluation metrics in ML.
method Integrates ecological and quantum statistical mechanics concepts to define Vendi Score.
result Vendi Score enables flexible diversity evaluation without requiring a reference dataset.
The paper analyzes how guidance affects diffusion models using Gaussian mixture models.
problem Understanding how guidance influences diffusion models in specific contexts.
method Theoretical study using Gaussian mixture models and comparison inequalities for differential equations.
result Guidance boosts classification confidence but reduces distribution diversity, leading to lower differential entropy.
A new method optimises problems with both continuous and categorical inputs.
problem Optimising black-box problems with mixed continuous and categorical inputs.
method Continuous and Categorical Bayesian Optimisation (CoCaBO) combining multi-armed bandits and Bayesian optimisation.
result CoCaBO outperforms existing methods on synthetic and real-world tasks.
ODS improves adversarial attacks by maximizing output diversity.
problem Efficiency and effectiveness of adversarial attacks, especially black-box attacks.
method Output Diversified Sampling (ODS) that maximizes diversity in model outputs.
result ODS reduces the number of queries needed for black-box attacks on ImageNet by a factor of two.
Deep learning models converge to Gaussian dynamics with mixed structured inputs.
problem Understanding neural network dynamics with complex input distributions.
method Extended hidden manifold model to Gaussian mixtures, analyzed via SGD.
result Learning dynamics with mixed inputs converge to Gaussian behavior.
Unified framework for portfolio optimization using multiple hypotheses.
problem Risk diversification in portfolio allocation.
method Structured ensemble learning approach with diversity control.
result Structured ensembles link predictor diversity to risk diversification.
DDN dynamically combines weights for domain-specific models, improving performance.
problem Limited compute resources for edge devices.
method Domain-aware Dynamic Network (DDN) that combines specialized weights based on input domain.
result DDN achieves up to 2.6% higher AP50 than a static network on the BDD100K benchmark.
Proposes training neural networks to predict uncertainty for out-of-distribution inputs.
problem Poor uncertainty predictions for out-of-distribution inputs limit model robustness.
method Generates pseudo-inputs in low-density regions and trains a Bayesian framework.
result Yields robust and interpretable uncertainty predictions.
New approach corrects image bias without labels.
problem Image search results skew towards majority groups.
method Uses visibly diverse control set to select images.
result Significantly improves visible diversity of results.
Proposes DC3-GAN for diverse unsupervised conditional generation.
problem Low diversity in unsupervised conditional generation.
method Integrates encoder-generator pair with generator-encoder pair to enhance diversity.
result Improves clustering performance and disentanglement of latent variables.
Generative model learns diverse generations using DPP to avoid mode collapse.
problem Mode collapse in generative models, limiting output to few modes.
method Inspired by DPP, proposes an unsupervised penalty loss to enhance diversity.
result Generative DPP approach consistently resists mode-collapse and outperforms state-of-the-art methods.
Study forecasts monthly electricity demand using pattern similarity-based methods.
problem Forecasting monthly electricity demand accurately.
method Pattern similarity-based forecasting methods (PSFMs) including k-NN, fuzzy, kernel regression, and GRNN.
result Ensemble models outperform individual PSFMs in forecasting accuracy.
Paper introduces a new model to handle multi-task learning across different input domains.
problem Learning correlated tasks across varying input domains.
method Develops a novel heterogeneous stochastic variational linear model of coregionalization (HSVLMC) for multi-task learning.
result The proposed model outperforms existing models in diverse multi-task scenarios.
The study provides precise asymptotic theory for in-context learning by Transformers.
problem Understanding the sample complexity, pretraining task diversity, and context length for successful in-context learning.
method An exactly solvable model of linear regression task by linear attention, deriving sharp asymptotics.
result Double-descent learning curve with increasing pretraining examples, phase transition between low and high task diversity regimes.
Despite recent advances, large scale visual artifacts are still a common occurrence in images generated by GANs. Previous work has focused on improving the generator's capability to accurately imitate the data distribution pdata. In this paper, we instead explore methods that enable GANs to actively avoid errors b…
VIDON learns operators with variable sensors, overcoming sensor limitations.
problem Fixed sensor locations restrict operator learning applicability.
method Variable-Input Deep Operator Network (VIDON) with random, varying sensors.
result VIDON efficiently approximates operators in PDEs and is robust to sensor permutations.
A method to improve image synthesis diversity using mutual information.
problem Mode collapse in conditional GANs for multimodal image synthesis.
method Explicitly estimate and maximize mutual information between latent code and output image.
result Prevents mode collapse and encourages synthesis of diverse images.
Proposes MOC method for better counterfactual explanations in ML models.
problem Difficulties in balancing multiple objectives for counterfactual explanations.
method Translates counterfactual search into a multi-objective optimization problem.
result Returns diverse counterfactuals with different trade-offs and maintains feature diversity.
Two new ALR approaches based on GS reduce labeled samples needed for regression.
problem Need substantial labeled samples for regression models, but unlabeled samples are easy to collect.
method Proposes two new ALR approaches based on greedy sampling (GS) to select beneficial unlabeled samples.
result Extensive experiments on various datasets verified the effectiveness and robustness of the approaches.
New method designs joint initial noises for diffusion models to improve diversity and alignment.
problem Independent initial noises limit diversity in generated images.
method Coupling of initial noises, maintaining Gaussian distribution while allowing dependence.
result Repulsive Gaussian coupling improves diversity without increasing sampling cost.