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.
Model combines long-term and short-term memory using conceptors.
problem Transfer between long-term and short-term memory.
method Recurrent neural network with gated reservoir for short-term memory and conceptors for long-term memory.
result Standard operations on conceptors allow combining long-term memories and describing their effect on short-term memory.
Paper proposes continual learning for sentence encoders.
problem Optimize sentence encoders for new corpora while maintaining old corpus accuracy.
method Initialize encoders with corpus-independent features, update using Boolean operations of conceptor matrices.
result Proposed sentence encoder can continually learn features from new corpora.
A new method detects change points in time series with conceptors.
problem Detecting change points in time series with nonlinear temporal dependence.
method Use of conceptor matrix to learn baseline dynamics and identify change points.
result The method provides a consistent estimate of the true change point.
A conceptor-based approach helps robots recognize human internal states.
problem Recognizing human internal states for diagnostic purposes in autism therapy.
method A conceptor-based classifier to classify internal states.
result Initial results show potential for detailed diagnostic information.
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.
Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials. This represents a rich source of information from which one can create value for people, organizations and businesses. For instance, recommender systems can benefit from automatically understanding…
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.
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…
Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and …
Paper proposes a new method for sentence embeddings using weighted word vectors.
problem Improving sentence embeddings for natural language processing tasks.
method A simple sentence embedding method using weighted average of word vectors followed by soft projection.
result Demonstrates effectiveness on clinical semantic textual similarity task.
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.
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.
Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, ho…
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…
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.
This paper evaluates debiasing methods on word embeddings to reduce religious bias.
problem Social biases persist in word embeddings, potentially amplifying them in AI applications.
method Investigates and evaluates three multiclass debiasing techniques on three word embeddings.
result ConceptorDebiasing is the most effective method, reducing religious bias by 82-96%.
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.
Coordinate descent methods usually minimize a cost function by updating a random decision variable (corresponding to one coordinate) at a time. Ideally, we would update the decision variable that yields the largest decrease in the cost function. However, finding this coordinate would require checking all of them, which…
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.
The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationships has led to LSTMs being characterized as black boxes. To this end, we introduce contextual decomposition (CD), an interpretation algorithm…
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.
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.
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.
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…
Most reinforcement learning algorithms are inefficient for learning multiple tasks in complex robotic systems, where different tasks share a set of actions. In such environments a compound policy may be learnt with shared neural network parameters, which performs multiple tasks concurrently. However such compound polic…
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.