HyperGAN generates diverse neural network parameters for improved performance and uncertainty.
problem Overconfidence of neural networks in out-of-distribution data.
method Generative model using a novel mixer to learn a distribution of neural network parameters.
result HyperGAN can generate parameters that perform competitively with fully supervised learning and provide better uncertainty estimates.
Enhances sample diversity in SGMCMC for better uncertainty estimation in BNNs.
problem Limited sample diversity in SGMCMC affects uncertainty estimation and model performance.
method Reparameterizes neural network weights to produce a more diverse set of samples.
result The proposed approach achieves superior performance in image classification tasks, including OOD robustness.
Hypernetworks are neural networks that generate weights for another neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We explain how this simple formulation gener…
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.
Single neural network predicts ImageNet model parameters for faster training.
problem Training diverse ImageNet models requires significant resources and time.
method Trained a neural network to predict ImageNet model parameters and used them for initialization.
result Models initialized with predicted parameters converge faster and achieve competitive performance.
Generative model creates diverse neural network weights efficiently.
problem Creating high-performance and diverse weights for neural networks.
method Trains a hypernetwork mapping latent vectors to high-performance weights, balancing accuracy and diversity.
result Generated weights form a diverse manifold, improving classification accuracy.
A co-evolutionary approach for Heston model calibration reduces overfitting with diverse datasets.
problem Overfitting and lack of generalization in Heston model calibration.
method Coupling a genetic algorithm with an evolving neural inverse map, using both GA-history sampling and Latin hypercube sampling.
result Diverse datasets improve out-of-sample stability and calibration accuracy.
Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired. While DPPs have many appealing properties, such as efficient sampling, learning the parameters of a DPP is still considered a difficult problem due to the non-convex nature…
SteinGen generates diverse graph samples from a single example.
problem Generating graphs with characteristic structures and diversity from a single example.
method Combines Stein's method and MCMC with Glauber dynamics and re-estimation of the Stein operator.
result High distributional similarity to the original data, combined with high sample diversity.
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.
DONNA rapidly finds optimal neural networks across diverse spaces.
problem Efficient scaling and handling of diverse architectural search-spaces in NAS.
method Three-phase pipeline: accuracy predictor, rapid evolutionary search, and optimal model finetuning.
result 100x faster than MNasNet in finding state-of-the-art architectures on-device.
Quality-Diversity algorithms explore multiple high-performing solutions in a search space.
problem Finding multiple high-performing solutions in complex optimization problems.
method Evolutionary computation approach focusing on behavioral space and holistic solution distribution.
result Quality-Diversity algorithms provide a comprehensive view of high-performing solutions in a search space.
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.
GRATIS generates diverse time series for benchmarking.
problem Lack of diverse time series data for evaluation.
method Uses mixture autoregressive (MAR) models to generate time series.
result Generates diverse and controllable time series efficiently.
Deep learning predicts neural network parameters efficiently.
problem Optimizing neural network parameters remains inefficient.
method Used graph neural networks to predict parameters of unseen networks.
result Achieved surprisingly good performance on unseen networks.
New method detects out-of-distribution samples in regression tasks.
problem Detecting instances far from training data in regression models.
method Estimating predictor entropy based on nearest neighbors and generative models.
result A new method for robust OOD detection in regression tasks.
The highly detailed international trade data among all countries in the world during 1971-2000 shows that the kinds of export goods and the logarithmic GDP (gross domestic production) of a country has an S-shaped relationship. This indicates all countries can be divided into three stages accordingly. First, the poor co…
Improves GANs by enforcing diverse feature learning.
problem GANs can collapse to a single configuration and be unstable.
method Enforces diverse feature learning by penalizing correlated features.
result Enforces diverse features, stabilizes training, and improves image synthesis.
CPRA efficiently finds diverse solutions in CO problems using UL and parallelization.
problem Finding optimal solutions often requires diverse outcomes in real-world applications.
method CPRA, an UL-based framework, discovers shared representations to generate diverse solutions.
result CPRA outperforms existing UL-based solvers in generating diverse solutions.
SEERL uses ensemble methods to improve reinforcement learning efficiency.
problem High sample complexity and computational expense in reinforcement learning.
method Directed perturbation of model parameters to learn diverse policies, selection of an adequately diverse set of policies.
result Our approach outperforms state-of-the-art scores in Atari 2600 and Mujoco.
Improved RL training for DMs reduces mode collapse and preserves diversity.
problem Mode collapse and training instability in RL fine-tuned diffusion models.
method Dynamic hierarchical RL training with sliding-window parameter regularisation.
result Models trained with HRF achieve better preservation of diversity in downstream tasks.
Proposes Ada-Sit method for mortality prediction of rare diseases.
problem Data insufficiency and clinical diversity of rare diseases make mortality prediction hard.
method Initialization-sharing multi-task learning method (Ada-Sit) for fast adaptation to similar tasks.
result Experimental results show the proposed model is effective for mortality prediction of diverse rare diseases.
Deep learning estimates time-varying Markov model parameters.
problem Estimating time-dependent parameters in Markov models.
method Reframes parameter estimation as an optimization problem using maximum likelihood.
result Real solution close to SDE with neural network-derived parameters under specific conditions.
Study shows not all ML models are uniquely identifiable from data.
problem Identifiability issues in machine learning models.
method Investigated through a case study on gait dynamics using a bipedal-spring mass model.
result Some parameters can be identified, but others remain unidentifiable.
Solves GAN mode collapse by assigning minibatches to multiple discriminators.
problem Mode collapse in GANs where models generate similar samples.
method Multiple discriminators, microbatching, and changing tasks.
result Promotes sample diversity in generated sets.
We analyze a negative-parameter variant of the diversity-weighted portfolio studied by Fernholz, Karatzas, and Kardaras (Finance Stoch 9(1):1-27, 2005), which invests in each company a fraction of wealth inversely proportional to the company's market weight (the ratio of its capitalization to that of the entire market)…
s-RBFN integrates multiple hypotheses for efficient and diverse prediction.
problem Integrating multiple hypotheses into learning models for regression.
method Structured Radial Basis Function Network (s-RBFN) using Voronoi tessellations and least-squares training.
result s-RBFN achieves superior generalization and efficiency compared to other models.
Automated discovery of diverse self-organized patterns in complex systems.
problem Automated identification of interesting spatially localized patterns in self-organizing systems.
method Intrinsically motivated machine learning algorithms (POP-IMGEPs) combined with deep auto-encoders and CPPN primitives.
result Efficiency and effectiveness of the proposed method in discovering diverse patterns compared to baselines.
Contrary to conventional economic growth theory, which reduces a country's output to one aggregate variable (GDP), product diversity is central to economic development, as recent 'economic complexity' research suggests. A country's product diversity reflects its diversity of knowhow or 'capabilities'. Researchers propo…
Study examines how decoding algorithms affect fairness in language generation models.
problem Impact of decoding algorithms on fairness in open-ended language generation.
method Systematic analysis of top-p, top-k, and temperature decoding algorithms. result Decoding algorithms significantly impact fairness across demographic groups.
Bayesian model fuses diverse microbiome data types.
problem Challenges in fusing different types of microbiome data.
method Flexible multinomial-Gaussian generative model with variational EM algorithm.
result Inferred latent variables provide common dimensionality reduction and predictive posterior distribution.
Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of learning the parameters (the kernel matrix) of a DPP from labeled training data. We make two contributions. First, we show how to reparameterize…
UCPO improves diversity in reinforcement learning models, maintaining high accuracy.
problem RLVR objectives often lead to diversity collapse, reducing coverage of correct solutions.
method UCPO adds a conditional uniformity penalty to GRPO, redistributing probability mass.
result UCPO improves Pass@K and diversity while maintaining competitive Pass@1 accuracy.
This paper introduces new invariants for time series analysis.
problem Analyzing the diversity and invariants of time series data.
method Introduces new invariants derived from the continuity of magnitude and maximum diversity.
result Demonstrates improved performance in machine learning experiments with real-world data.
Meta-learning performance is affected by how task diversity is allocated, not just overall variability.
problem Meta-learning performance degrades when task diversity is unevenly distributed.
method Decomposed task-specific regression effects into structurally informative and orthogonal components.
result Meta-learning prediction degrades when a larger fraction of task variability is orthogonal and non-informative.
Paper fine-tunes LLMs for financial tasks using data fusion.
problem Improving LLMs for financial analysis tasks.
method Fine-tuned Llama3-8B and Mistral-7B using PEFT and LoRA, combined datasets for data fusion.
result Enhanced model performance across financial tasks.
Paper uses Bayesian optimization to find best Supertrend indicator settings.
problem Finding optimal trading parameters for the Supertrend indicator.
method Bayesian optimization to automate parameter selection.
result BO-optimized Supertrend strategy yields higher profits in backtesting.
New algorithm reduces bandit problem's regret bound to logarithmic in dimension.
problem Sparse linear bandit problem with sparse reward structure.
method Proposes an algorithm that uses compatibility condition on optimal arm.
result Achieves regret bound of O(poly log dT) without additional diversity assumptions.
Adaptively sparse Transformers improve interpretability and diversity in NLP.
problem Standard Transformers use dense attention, limiting interpretability and diversity.
method Introduces adaptively sparse Transformers using α-entmax for context-dependent sparsity. result Improves interpretability and diversity in NLP tasks without sacrificing accuracy.
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.
Hydra distills ensemble models into a single model while preserving diversity and uncertainty.
problem Loss of ensemble diversity and uncertainty in distilled models.
method Single multi-headed neural network with shared body network.
result Hydra improves distillation performance and preserves ensemble diversity and uncertainty.
A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model…
Teacher algorithm helps DRL learn diverse environments efficiently.
problem Teach DRL to learn in various, unknown environments efficiently.
method Transformed into a bandit problem, learns to sample environments.
result ALP-GMM models learning progress, improving curriculum design.
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.
DIVA generates diverse tasks for complex simulators, enabling adaptive agent training.
problem Lack of diverse training data for complex, open-ended simulators.
method Evolutionary approach using domain randomization and procedural generation.
result Successfully trains adaptive agent behavior in complex simulators.
Meta metric learning improves few-shot learning for diverse domains.
problem Few-shot learning struggles with diverse domains and varying label numbers.
method Task-specific learners with metric learning and a meta learner to discover task-specific metrics.
result Meta metric learning achieves superior performance in diverse multi-domain tasks and flexible label numbers.
Random Forests don't overfit, challenging the double-descent theory.
problem The overfitting behavior of Random Forests.
method Challenged the double-descent curve theory by analyzing the performance of Random Forests and proposing a new approach to diversity in ensembles.
result Random Forests do not exhibit a double-descent curve but rather a single descent, indicating they do not overfit in the classic sense.
Graphs of neural networks are represented to preserve symmetry, improving performance across various tasks.
problem Lack of equivariance in neural network representations of other neural networks.
method Represent neural networks as computational graphs and use graph neural networks to preserve permutation symmetry.
result Single model encodes diverse neural architectures, outperforming state-of-the-art methods.