Improved negation detection in Dutch clinical texts using machine learning.
problem Extracting negation from clinical text for better model development.
method Comparison of rule-based and machine learning methods (biLSTM, RoBERTa).
result BiLSTM and RoBERTa models outperform rule-based method in F1 score, precision, and recall.
BiLSTM model improves NER and negation detection in radiological reports.
problem Automating medical information extraction from radiological reports.
method Bi-directional Long Short-Term Memory (BiLSTM) neural network architecture.
result BiLSTM outperforms traditional rule-based systems for NER and negation detection.
Paper tackles negations in information processing by replicating human behavior.
problem Difficulty in computers understanding negations in textual content.
method Reinforcement learning to replicate human perception of negations.
result Inferred policy can derive statistical inferences about human negation processing.
Convolutional neural network improves assertion detection in multi-label clinical text.
problem Detecting assertions in multi-label clinical text with rich descriptions.
method Developed a CNN architecture for multi-label scope detection.
result At least 12% improvement over state-of-the-art on multi-label clinical text.
QNNs can't distinguish binary signals from their negations, revealing a new symmetry.
problem Understanding the behavior of QNNs in binary pattern classification.
method Presented and analyzed a new form of invariance (negational symmetry) in QNNs.
result QNNs cannot differentiate a quantum binary signal and its negational counterpart in binary classification tasks.
This paper describes the resource- and system-building efforts of an eight-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation (SIMT). We describe a new modality/negation (MN) annotation schem…
Deep EHR predicts chronic diseases using medical notes and structured data.
problem Early detection of chronic diseases for better management and resource allocation.
method Proposes a multi-task framework combining free-text medical notes and structured EHR data using deep learning.
result Deep learning models using text outperform models using only structured data, and models with numerical values and negations in text perform best.
Bayesian method detects neuron spike activities from noisy fluorescence data.
problem Detecting neuron spike activities from noisy fluorescence data.
method Random finite set (RFS) based Bayesian approach.
result Gains 12% extra detection accuracy compared to MLSpike method.
Systematic review of ML models for detecting social media deception.
problem Detecting fake news, spam, and fake accounts on social media.
method 36 studies evaluated using PROBAST tool, identifying biases and limitations.
result Over-reliance on accuracy in imbalanced data settings is a flaw.
Deep learning detects cloud changes due to human aerosols.
problem Uncertainty in the effect of anthropogenic aerosols on cloud properties and Earth's energy balance.
method Deep convolutional neural networks to analyze cloud images.
result Identified and characterized specific cloud perturbations due to human aerosols.
New method detects when models influence their own drift in real-time data streams.
problem Models can induce concept drift in real-time data streams.
method CheckerBoard Performative Drift Detection (CB-PDD)
result CB-PDD effectively detects performative drift in real-time data streams.
Study shows some complex shapes don't fit a certain property.
problem Some aspherical manifolds lack the Bounded Index Property (BIP).
method Analyzes specific aspherical manifolds to prove they don't have BIP.
result Proves existence of aspherical manifolds without BIP.
Unified framework evaluates different nearest neighbor classification methods.
problem Evaluating and comparing classical, fuzzy, and fuzzy rough nearest neighbor classification methods.
method Standardized nearest neighbor weighting with kernel functions applied to distance and/or rank values of nearest neighbors.
result NN, FNN, and FRNN perform best with Boscovich distance, and NN and FRNN perform best with specific combinations of weights and scaling measures.
Unsupervised method improves word vectors by suppressing high variance features.
problem Improving semantic information in word vectors.
method Using conceptors to suppress high variance features in word vectors.
result Post-processed word vectors outperform existing alternatives in lexical evaluation tasks.
This study analyzes how RNNs process context in sentiment analysis.
problem Understanding how recurrent neural networks process context in sentiment analysis.
method Developed methods to reverse engineer RNNs, identifying contextual effects and quantifying their strength and timescale.
result Identified inputs that induce contextual effects and quantified their properties.
It is a conjecture that the signature of a positive link is bounded below by an increasing function of its negated Euler characteristic. In relation to this conjecture, we apply the generator description for canonical genus to show that the boundedness of the genera of positive knots with given signature can be algorit…
Study how predictions affect the data they're based on, improving generalization guarantees.
problem How well do models generalize when predictions influence the data they're trained on?
method Embed performative predictions into statistical learning theory and prove generalization bounds.
result There's a fundamental trade-off between affecting data and learning from it.
The scientific data about the state of our planet, presented at the 2012 (Rio+20) summit, documented that today's human family lives even less sustainably than it did in 1992. The data indicate furthermore that the environmental impacts from our current economic activities are so large, that we are approaching situatio…
PolySwarm uses a swarm of LLMs to predict and arbitrage prediction markets.
problem Real-time prediction market trading and latency arbitrage inefficiencies.
method PolySwarm employs a swarm of 50 diverse LLMs, Bayesian combination, and risk-controlled execution.
result Swarm aggregation outperforms single-model baselines in prediction tasks.
AWARE-FX uses AI to audit foreign-exchange risk disclosures in corporate reports.
problem Weakly structured foreign-exchange risk disclosures in corporate reports.
method Combines lexicon, logic, encoders, and aggregation methods to convert text into traceable measures.
result FinBERT outperforms in most comparisons, improving F1 scores by up to 0.077.
Efficient oblique RSF method improves prediction and interpretability.
problem Limited computational efficiency and difficulty in interpreting oblique RSF ensembles.
method Newton-Raphson scoring for computational efficiency and negation importance for variable importance estimation.
result The method reduces computational overhead by 450 times and improves prediction accuracy.
The paper analyzes how CNNs interpret NLP tasks and identify linguistic features.
problem Understanding how CNNs capture linguistic features in NLP tasks.
method Visualization techniques and error analysis to interpret CNNs.
result Identified how CNNs capture different linguistic features and their impact on model performance.
Mahé provides hierarchical explanations for complex interactions in machine learning models.
problem Capturing and explaining complex interactions in machine learning models.
method Model-agnostic hierarchical explanations through local interpretation and context-free generalization.
result Improved local interaction interpretations and successful explanation of context-free interactions.
Neuro-symbolic agent learns systematic generalisation from formal instructions.
problem Achieving zero-shot generalisation of formally specified tasks.
method Combines deep reinforcement learning with temporal logic.
result Systematic learning emerges with convolutional layers and abstract operators.
DAL improves active learning for neural networks with large batch sizes.
problem Efficiently choosing examples to label for neural networks with large batch sizes.
method DAL treats active learning as a binary classification task to make labeled and unlabeled sets indistinguishable.
result DAL performs on par with state-of-the-art methods in medium and large query batch sizes.
We fully describe the horofunction boundary ∂hL2 with the word metric associated with the generating set {t,at} (i.e the metric arising in the Diestel-Leader graph DL(2,2)). The visual boundary ∂∞L2 with this metric is a subset of ∂hL2. Although $\partial_\infty L_2…
Sign equivariant networks improve model expressiveness for spectral geometric learning.
problem Limited expressiveness of sign invariant models for tasks like graph link prediction.
method Developed sign equivariant neural network architectures based on new analytic sign equivariant polynomials.
result Sign equivariant models achieve theoretical benefits in spectral geometric learning tasks.
Enhances quantum machine learning models using Fock states.
problem Data-embedding bottleneck in quantum machine learning.
method Photonic-based bosonic data-encoding scheme in Fock space.
result Controlled expressive power via photon number.
UTE improves reinforcement learning by measuring action uncertainty, enhancing policy learning efficiency.
problem Degrading performance of action repetition in reinforcement learning, especially with sub-optimal actions.
method UTE uses ensemble methods to measure uncertainty during action extension, allowing strategic exploration or certainty.
result UTE outperforms existing action repetition algorithms, significantly enhancing policy learning efficiency.
Active learning can't improve over passive in certain settings.
problem Active learning vs. passive learning in nonparametric settings.
method Analyzing margin conditions and their effects on active learning performance.
result Nuances in margin conditions determine whether active learning can outperform passive learning.
Optimized deep learning architectures improve sensor fusion performance.
problem Sensor fusion in autonomous systems.
method Proposed two optimized architectures: coarser-grained and two-stage gated.
result Significant performance improvements and robustness in noisy conditions.
A new method improves coordinate descent by adaptively selecting coordinates.
problem Coordinate descent's inefficiency due to checking all coordinates.
method Adaptive multi-armed bandit algorithm to select coordinates.
result Improves convergence of coordinate descent methods.
Paper introduces compressibility loss for learning sparse neural network weights.
problem Learning highly compressible neural network weights.
method Applying a compressibility loss to minimize the negated sparsity of the signal.
result At critical points, weight vectors are ternary signals with a sparsity directly related to the objective value.
Energy-based models can generate complex images by combining simpler concepts.
problem Generating natural images that satisfy complex logical combinations of concepts.
method Energy-based models combine probability distributions of simpler concepts to generate compositions.
result Energy-based models can generate images that satisfy conjunctions, disjunctions, and negations of concepts.
A Boolean algebra formalizes task composition for reinforcement learning.
problem Formalizing task composition for efficient learning and problem-solving.
method Formalized tasks as a Boolean algebra, learning goal-oriented value functions, and composing them to solve new tasks.
result Agents can solve new tasks without additional learning by composing value functions in specific ways.
DVE uses GPs on DNN outputs to provide UQ without retraining.
problem Feature collapse in DNNs affects UQ methods.
method Deep Vecchia ensemble (DVE) of GPs on DNN hidden layers.
result Deterministic UQ possible in feature-collapsed DNNs.
Statistical model checking for PCTL on MDPs using reinforcement learning.
problem Model checking PCTL specifications on MDPs with statistical methods.
method Reinforcement learning for policy search, statistical model checking with UCB-based Q-learning.
result Provably guaranteed statistical model checking method for PCTL specifications on MDPs.
CD interprets LSTM predictions by identifying word interactions.
problem LSTMs are black boxes; understanding their internal workings is difficult.
method Contextual decomposition (CD) to interpret LSTM predictions.
result CD reliably identifies word interactions and sentiment combinations.
Let R be a real closed field, Q⊂R[Y1,...,Yℓ,X1,...,Xk], with $ °_{Y}(Q) \leq 2, °_{X}(Q) \leq d, Q \in {\mathcal Q}, #({\mathcal Q})=m$, and P⊂R[X1,...,Xk] with $°_{X}(P) \leq d, P \in {\mathcal P}, #({\mathcal P})=s$. Let S⊂Rℓ+k be a semi-alg…
Systematically compares methods for explaining RNN predictions.
problem Understanding the decisions of RNNs, particularly LSTMs, through relevance assignments.
method Systematic comparison of methods in various settings.
result Best method reveals linguistic phenomena in sentiment analysis.
Antithetic noise improves diffusion models' uncertainty quantification.
problem Improving uncertainty quantification in diffusion models.
method Pairing each noise sample with its negation, leading to strong negative correlation.
result Substantially more reliable uncertainty quantification with up to 90% narrower confidence intervals.
We prove a nearly optimal bound on the number of stable homotopy types occurring in a k-parameter semi-algebraic family of sets in Rℓ, each defined in terms of m quadratic inequalities. Our bound is exponential in k and m, but polynomial in ℓ. More precisely, we prove the following. Let R be a real close…
Let R be a real closed field, Q⊂R[Y1,...,Yℓ,X1,...,Xk], with $ °_{Y}(Q) \leq 2, °_{X}(Q) \leq d, Q \in {\mathcal Q}, #({\mathcal Q})=m,$ and P⊂R[X1,...,Xk] with $°_{X}(P) \leq d, P \in {\mathcal P}, #({\mathcal P})=s$, and S⊂Rℓ+k a semi-algebr…
Entropy asymmetry affects regularization in ERM, leading to biased solutions.
problem Analyzing the impact of relative entropy asymmetry in ERM regularization.
method Examined Type-I and Type-II ERM-RER, comparing their solutions and properties.
result Type-II ERM-RER regularization introduces a strong bias against training data.
Poly-GNNs achieve similar performance regardless of depth, highlighting graph noise's dominance.
problem Performance of poly-GNNs in semi-supervised node classification.
method Analysis of poly-GNNs under a contextual stochastic block model (CSBM).
result For a sufficiently large graph, depth k>1 poly-GNNs exhibit the same rate of separation as depth k=1 counterparts. In the present work some generalizations of the Hawking singularity theorems in the context of f(R) theories are presented. The assumptions are of these generalized theorems is that the matter fields satisfy the conditions (Tij−2gijT)kikj≥0 for any generic unit time like field, that…
DiGrad improves multi-task reinforcement learning in robotic systems.
problem Efficient multi-task reinforcement learning in complex robotic systems with shared actions.
method Differential Policy Gradient (DiGrad) for simultaneous training of multiple tasks in a single actor-critic network.
result DiGrad outperforms related methods in continuous action spaces, supporting efficient multi-task learning.
New algorithms improve automated text sentiment analysis.
problem Automated classification of text sentiment.
method Two new Genetic Algorithms (GAs) for identifying sentiment and amplifier words in text.
result Our approach outperformed existing algorithms in sentiment analysis experiments.