YC Bench forecasts startup success in Y Combinator batches with a short-term metric.
problem Difficult forecasting of startup success due to sparse meaningful outcomes and slow evaluation cycles.
method Developed a live benchmark using publicly available traction signals and web visibility metrics.
result Revealed 6 out of 11 top performers at YC Demo Day with a simple proxy for prior brand recognition.
Prediction markets can shape political behavior through persistent signals, not just forecast accuracy.
problem The role of prediction markets beyond forecasting.
method Transaction-level evidence from the 2024 U.S. presidential election, Signal Credibility Index (SCI).
result Price signals in prediction markets are more influential due to persistence, breadth of trader types, and cross-platform consensus.
The paper connects eigenvalue problems for various operators and establishes inequalities and asymptotic formulas for heat traces.
problem Eigenvalue problems and heat trace asymptotics for different operators.
method Establishes connections and inequalities for eigenvalues and heat traces.
result Eigenvalue inequalities and three-term asymptotic formulas for heat traces of various operators.
This paper presents a new meta-modeling framework to employ deep reinforcement learning (DRL) to generate mechanical constitutive models for interfaces. The constitutive models are conceptualized as information flow in directed graphs. The process of writing constitutive models are simplified as a sequence of forming g…
Solves time-optimal navigation on slippery slopes with cross gravitational wind.
problem Time-optimal navigation on a slippery cross slope under gravitational wind.
method New Finsler metric derived for the problem, considering both lateral and longitudinal gravitational effects.
result Conditions for strong convexity and purely geometric solution provided.
Articulatory distinctive features, as well as phonetic transcription, play important role in speech-related tasks: computer-assisted pronunciation training, text-to-speech conversion (TTS), studying speech production mechanisms, speech recognition for low-resourced languages. End-to-end approaches to speech-related tas…
Echocardiography (echo) is a common means of evaluating cardiac conditions. Due to the label scarcity, semi-supervised paradigms in automated echo analysis are getting traction. One of the most sought-after problems in echo is the segmentation of cardiac structures (e.g. chambers). Accordingly, we propose an echocardio…
The word2vec software of Tomas Mikolov and colleagues (https://code.google.com/p/word2vec/ ) has gained a lot of traction lately, and provides state-of-the-art word embeddings. The learning models behind the software are described in two research papers. We found the description of the models in these papers to be some…
We develop theory and computational methods to investigate particle inclusions embedded within curved lipid bilayer membranes. We consider the case of spherical lipid vesicles where inclusion particles are coupled through (i) intramembrane hydrodynamics, (ii) traction stresses with the external and trapped solvent flui…
The paper provides Gaussian approximations for decentralized Federated Learning.
problem Lack of asymptotic statistical guarantees for local SGD in Federated Learning.
method Two generalized Gaussian approximation results for local SGD trajectories.
result Valid multiplier bootstrap procedures and Gaussian bootstrap-based tests for detecting adversarial attacks.
pySigLib speeds up signature-based computations on CPUs and GPUs.
problem Efficient signature-based computations on large datasets and long sequences.
method Optimised Python library for CPU and GPU, novel differentiation scheme.
result Accurate gradients at a fraction of the runtime of existing libraries.
Bayesian optimization simplifies bioprocess engineering experiments.
problem Complex biological systems and experimental uncertainty.
method Adapts classical Bayesian optimization for bioprocess engineering.
result Provides accessible introduction to Bayesian optimization for practitioners.
Graph neural networks improve solving linear optimization problems.
problem Improving the efficiency of solving linear optimization problems.
method Using graph neural networks to simulate standard interior-point methods for linear optimization problems.
result Graph neural networks can solve linear optimization problems close to optimality, often outperforming conventional solvers.
The paper develops a method to discover causal relations and predict material laws with uncertainty quantification.
problem Discovering causal relations and predicting material laws with uncertainty in civil engineering applications.
method The paper develops a causal discovery algorithm to infer causal relations among time-history data. It uses a deep neural network with dropout layers for uncertainty quantification and propagates predictions through a causal graph.
result The method accurately predicts material laws and quantifies uncertainty, as demonstrated in two numerical examples.
Unified approach unites GANs and diffusion models using particle methods.
problem Combining GANs and diffusion models for generative tasks.
method Proposes a unified framework where generator training is seen as a generalization of particle models.
result Demonstrates that GANs and diffusion models can be integrated within a unified framework.
Deep Learning is gaining traction with geophysics community to understand subsurface structures, such as fault detection or salt body in seismic data. This study describes using deep learning method for iceberg or ship recognition with synthetic aperture radar (SAR) data. Drifting icebergs pose a potential threat to ac…
GCNs learn by embedding similar nodes within a class and leveraging consistent neighborhood structures.
problem Understanding how GCNs perform semi-supervised node classification on both homophilous and heterophilous graphs.
method Investigated the latent node embeddings and neighborhood structures of GCNs.
result GCNs learn by embedding similar nodes within a class and leveraging consistent neighborhood structures.
New Gram determinant from Möbius band connects to annulus case.
problem Exploring new Gram determinants in knot theory.
method Skein theoretic approach and bilinear forms.
result Proves important results about new Gram determinant structure.
Deep generative models are rapidly gaining traction in medical imaging. Nonetheless, most generative architectures struggle to capture the underlying probability distributions of volumetric data, exhibit convergence problems, and offer no robust indices of model uncertainty. By comparison, the autoregressive generative…
New gradient estimator improves training for normalizing flows.
problem Training normalizing flows for complex models.
method Developed a new gradient estimator for Stochastic Gradient Descent.
result Significantly faster and more precise training for φ^4 model.
Survey of deep learning models in finance.
problem Improving financial models with deep learning.
method Categorized and analyzed financial applications of deep learning models.
result Outperformance of deep learning over classical models in finance.
Unified physics-informed learning method improves generalization performance.
problem Lack of theoretical analysis for hybrid settings with incomplete physical constraints.
method Unified residual form unifying collocation and variational methods, establishing generalization performance governed by affine variety dimension.
result Generalization performance is determined by affine variety dimension, not just the number of parameters.
Improved hypernetwork for efficient neural network hyperparameter tuning.
problem Efficiently optimizing hyperparameters in neural networks.
method Proposed Δ-STN architecture focusing on accurate best-response Jacobian approximation. result Significantly improved hyperparameter tuning accuracy and stability.
Perplexity fails to distinguish correct predictions from incorrect ones in some cases.
problem Perplexity's limitations in model selection.
method Analyzing Transformer continuity and iso-perplexity plots.
result Perplexity does not always select the correct model.
Unified RL survey for healthcare AI interventions.
problem Limited real-life application of RL in healthcare.
method Unified technical survey and case studies.
result Bridge between dynamic treatment regimes and mobile health.
Paper presents privacy-preserving techniques for HD computing.
problem Privacy loss in HD computing due to reversible computation.
method Quantization and pruning of hypervectors for differential privacy.
result Differentially private HD model for cloud inference.
With the spreading prevalence of Big Data, many advances have recently been made in this field. Frameworks such as Apache Hadoop and Apache Spark have gained a lot of traction over the past decades and have become massively popular, especially in industries. It is becoming increasingly evident that effective big data a…
The paper tackles scalability issues in Graph Representation Learning.
problem Prohibitive time and memory complexities in Graph Representation Learning.
method Leveraging the K-Core Decomposition property of Graphs to reduce time and memory consumption.
result Proposed techniques significantly reduce computational resources without compromising embedding quality.
This work proposes hyperbolic deep convolutional neural networks for better pattern recognition.
problem The limitations of Euclidean deep convolutional neural networks in capturing intricate patterns.
method Developed Hyperbolic DCNN based on Poincaré Disc, analyzing expansive convolution in non-Euclidean space.
result Hyperbolic convolutional architecture outperforms Euclidean ones in pattern recognition tasks.
HedgeAgents boosts financial trading with balanced strategies.
problem Inefficient trading strategies under rapid market changes.
method Integrates LLMs with multi-agent system for robust decision-making.
result 70% annualized return and 400% total return over 3 years.
Hybrid quantum-classical RL model solves standard benchmark tasks and proves quantum advantage.
problem Challenges in reinforcement learning, especially in solving standard benchmarking tasks.
method Parametrized quantum circuits in a hybrid quantum-classical RL model.
result Demonstrates quantum advantage in solving standard benchmarking tasks and intractable classical problems.
A new geometry for comparing signals, overcoming traditional limitations.
problem Comparing and interpolating discontinuous and signed signals.
method Investigation of Riemannian geometry on signal space, introducing a metric that measures both horizontal and vertical deformations.
result Characterization of metric properties and establishment of geodesic regularity and stability.
This paper analyzes arbitrage opportunities in Polymarket's NBA markets.
problem Underexplored market microstructure and high-frequency pricing efficiency in decentralized prediction markets.
method Systematic empirical analysis of algorithmic arbitrage using over 75 million limit order book snapshots.
result Microstructural efficiency is profound, with single-market anomalies rare and combinatorial inefficiencies more frequent.
This research compiles knowledge on decentralized exchanges with AMM protocols.
problem Improving and developing AMM-based decentralized exchanges.
method Established a general AMM framework, compared mechanics, discussed security and privacy.
result Illustrated conservation and slippage functions of AMM protocols.
Neuro-Evolution is a field of study that has recently gained significantly increased traction in the deep learning community. It combines deep neural networks and evolutionary algorithms to improve and/or automate the construction of neural networks. Recent Neuro-Evolution approaches have shown promising results, rival…
Paper presents a unique method to recover signals from their bispectrum.
problem Retrieving signals accurately from their bispectrum.
method Two-step trust region algorithm that minimizes a non-convex objective function.
result Signals with finite spectral or temporal support can be recovered from at least 3B measurements of their bispectrum.
Paper proposes efficient methods for clustering and signal recovery in high-dimensional data with block structures.
problem High-dimensional clustering and signal recovery under block signal structures.
method CFA-PCA and MA-PCA methods for sparse and dense block signals.
result Proposed methods achieve computational minimax optimality for clustering and signal recovery.
Deep learning improves community detection in graph datasets.
problem Community detection in graph datasets using deep learning.
method Proposes a deep learning approach using Gumbel Softmax for clustering graph nodes.
result The new approach significantly outperforms traditional clustering methods.
Unified framework for multi-view autoencoders simplifies model implementation and evaluation.
problem Inconsistent notation and implementation of multi-view autoencoders.
method Unified mathematical framework, documentation, and Python library.
result Comparable or superior performance in benchmarking experiments.
Proposes a novel graph signal model using narrowband kernels.
problem Graph signals with multiple concentrated frequency regions.
method Jointly learns graph signal model parameters and coefficients.
result Joint learning improves signal interpolation accuracy.
VaSST uses soft symbolic trees for probabilistic symbolic regression.
problem Efficiently recover symbolic expressions from noisy data.
method Variational inference with soft symbolic trees.
result Superior performance in structural recovery and predictive accuracy.
Solves time-minimizing navigation on a mountain slope using Riemann-Finsler geometry.
problem Time-minimizing navigation on a mountain slope under gravity.
method Riemann-Finsler geometry, Zermelo navigation problem, anisotropic deformation of the background Riemannian metric, rescaled gravitational wind.
result A new Finsler metric for optimal navigation on slippery mountain slopes.
Paper estimates FPR of Bayes classifier using soft labels.
problem Determining optimal classifier performance.
method Uses soft labels and denoising technique.
result Consistent and unbiased FPR estimator developed.
Improved exploration in RL with latent state marginalization.
problem Complexity of deep probabilistic models limits their practical use in reinforcement learning.
method Adopting latent variable policies within the MaxEnt framework, with low-cost marginalization of latent states.
result Effective marginalization leads to better exploration and more robust training.
Machine Learning has been steadily gaining traction for its use in Anomaly-based Network Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed using the KDD~CUP~99 dataset as a benchmark. Several studies question its usability while constructing a contemporary NIDS, due to the skewed r…
New framework models graph signals as distribution-valued signals in Wasserstein space.
problem Limitations of classical vector-based GSP, including synchronous observations and uncertainty.
method Introduces graph distribution-valued signals (GDSs) in the Wasserstein space.
result GDSs naturally encode uncertainty and stochasticity, generalizing traditional graph signals.
The paper explores how to measure and optimize ad reach while maintaining user privacy.
problem Measuring ad reach while preserving user privacy in online advertising.
method Introduces k-anonymity and probabilistic discounting for frequency capping. result Privacy introduces a significant performance drop but with manageable costs.
New algorithms improve signal processing in federated learning.
problem Efficiently process distributed signal samples with privacy and communication constraints.
method Proposes overpredictive signal approximations using convex optimization.
result Quantifies tradeoffs between communication cost, sampling rate, and approximation error.