We further study the incidence relations that arise from the various subtowers, known as Baby Monster, which exist within the -Monster Tower. This allows us to complete the class spelling rules. We also present a method of calculating the various Baby Monster that appear within the Monster Tower.
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48 results for “spelling rules”
Study spells for points in a three-dimensional Monster tower.
problem Determine spelling rules for points in the Monster tower.
method Analyze incidence relations between Baby Monsters and derive spelling rules.
result Determine precisely which words are realized by points in the tower.
Real-time spell checker adapts to new languages.
problem No real-time, language-adaptable spell checkers for non-English languages.
method Used Wikipedia and subtitles data to generate dictionaries, created noisy channel datasets, compared with industry tools.
result System performs well across 24 languages, outperforming existing tools.
Study on financial crises duration and volatility in US markets.
problem Duration of negative stock market returns and its impact on volatility.
method Survival models, log-normal distribution, continuous time analysis.
result Conditional probability of ending negative return spells increases up to 2-3 months after onset.
Novel approach to estimate P300 BCI efficiency using SNR.
problem Improving the accuracy of P300 BCI for severely disabled people.
method Introduced a novel approach considering P300 SNR for estimating efficiency, using a Gaussian noise model.
result P300 SNR significantly correlates with spelling accuracy, improving BCI efficiency.
A transformer model improves spell correction with hierarchical attention.
problem Improving spell correction accuracy and speed.
method Multi encoder-single decoder transformer architecture with hierarchical attention.
result Significant improvement in CER, WER, and SER error rates.
Synthetic noise training improves machine translation robustness to spelling mistakes.
problem Making machine translation robust to spelling mistakes and natural noise.
method Training on synthetic noise to improve robustness to natural noise.
result Training on synthetic noise improves robustness to natural noise without diminishing performance on clean text.
End-to-end ASR system uses context n-grams for better speech recognition.
problem Contextual information impacts speech recognition accuracy.
method Jointly optimizes ASR components with context embeddings during inference.
result Proposed CLAS system outperforms traditional methods by 68% relative WER.
Chatbot uses BERT to handle financial investment questions, improving accuracy and decision-making.
problem Improving accuracy and decision-making in financial investment customer service.
method Deep Bidirectional Transformer (BERT) model, uncertainty measure comparison, mixed-integer programming, automatic spelling correction.
result Chatbot can recognize 381 intents and decide when to escalate questions.
We present Listen, Attend and Spell (LAS), a neural network that learns to transcribe speech utterances to characters. Unlike traditional DNN-HMM models, this model learns all the components of a speech recognizer jointly. Our system has two components: a listener and a speller. The listener is a pyramidal recurrent ne…
Winterization of Texas power system profitable but risky, estimated at $11.74bn over 30 years.
problem Profitability and risk of winterizing Texas power system infrastructure.
method Combined temperature-dependent load and outage estimates over 71 years of climate data.
result Large-scale winterization of gas infrastructure and power plants is profitable, but risks are high due to low-frequency of cold spells.
Improves diversity of text-to-image models without sacrificing FID.
problem Lack of diversity and tendency to recreate training set images.
method Adds sparse repellency terms to diffusion SDE to guide trajectories away from a reference set.
result Improves diversity of diffusion models with minimal impact on FID.
Grapheme ASR improves with G2G model that corrects spelling errors.
problem Rare long-tail words in non-phonemic languages like English.
method Train G2G model on text-to-speech data to rewrite character sequences into phonetically consistent forms.
result Reduces Word Error Rate by 3% to 11% over a strong graphemic baseline.
Extensions of Lie algebrasmath.DG
We review (non-abelian) extensions of a given Lie algebra, identify a 3-dimensional cohomological obstruction to the existence of extensions. A striking analogy to the setting of covariant exterior derivatives, curvature, and the Bianchi identity in differential geometry is spelled out. In the new version references ad…
A new transformer model corrects diacritics and typos in multiple languages.
problem Restoring diacritics and correcting typos in online communications.
method Employing a universal ByT5 transformer model trained on 12 languages, including Lithuanian.
result Achieves > 98% accuracy in diacritics restoration and typos correction.
In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently, there has been a growing interest in understanding how more data can be leveraged to reduce the required training runtime. In this paper, we…
The universal perturbative invariants of rational homology spheres can be extracted from the Chern-Simons partition function by combining perturbative and nonperturbative results. We spell out the general procedure to compute these invariants, and we work out in detail the case of Seifert spaces. By extending some prev…
Study modular class of Lie ∞-algebroids and their adjoint actions.
problem Understanding the modular class and adjoint actions of Lie ∞-algebroids.
method Equivalence of descriptions, homotopy invariance, explicit actions and dualities.
result Homotopy invariance of modular classes and explicit adjoint actions.
New method reduces calibration time for BCI with theoretical guarantees.
problem Reducing or eliminating calibration time for BCI users.
method Learning from label proportions (LLP) for online unsupervised classification.
result LLP classifier achieves 84.5% correct character spelling without prior calibration.
The paper defines and studies the category of Z-graded manifolds, including their intrinsic structure and formal properties.
problem Understanding the categorical properties and intrinsic structure of Z-graded manifolds.
method Describing local models, explaining formality, and formulating analogues of theorems.
result Proper definitions of objects and morphisms in the category of Z-graded manifolds, and formulation of Batchelor's theorem.
End-to-end model detects articulatory features from speech data.
problem Detecting articulatory features from speech data for various applications.
method Apply Listen, Attend and Spell (LAS) architecture and attention models.
result End-to-end training of manners and places of articulation detectors.
Paper trains models to resist string transformations.
problem Vulnerability of NLP models to adversarial string transformations.
method Combines search and abstraction techniques for robust training.
result Trained models resist combinations of user-defined transformations.
Paper justifies regularization in machine learning using Occam's razor.
problem Justifying Occam's razor in machine learning.
method Statistical learning theory to justify preference for simplicity over fit.
result Justification for regularization in machine learning.
AV-ASR system improves speech recognition with visual context.
problem Improving speech recognition accuracy with visual information.
method Transformer-based architecture with multiresolution and multimodal training.
result Multiresolution training speeds up convergence and improves WER by 18%.
Improved online neural transducer model matches non-streaming model performance.
problem Significant performance degradation of online neural transducer models.
method Increased attention window, LAS initialization, stronger language models.
result Improved online neural transducer model matches non-streaming model performance.
Diffusion models can memorize training data, limiting their creativity and privacy.
problem Memorization in diffusion models that reproduces training data instead of generating novel outputs.
method Dual-separation approach via statistical estimation and network approximation.
result Pruning-based method reduces memorization while maintaining generation quality.
There are two themes in the present paper. The first one is spelled out in the title, and is inspired by an attempt to find an analogue of Hersch-Yang-Yau estimate for of surfaces in symplectic category. In particular we prove that every split symplectic manifold admits a compatible Riemannian …
New derivation shows how a three-factor learning rule is derived from Oja's rule.
problem Deriving a three-factor learning rule from Oja's rule.
method Using frame theory to systematically derive EGHR-PCA from Oja's rule.
result A principled derivation of a biologically plausible learning rule.
Model shows how banks' fears of future defaults can cause immediate financial stress.
problem How banks' future default worries cause immediate financial stress.
method Dynamic interbank model with endogenous distress contagion, mark-to-market valuation adjustment, forward-backward approach.
result Distress contagion acts as a stochastic volatility term leading to clustering and down-market spikes.
We develop a Chern character map for twisted equivariant non-abelian cohomology.
problem Understanding non-abelian cohomology theories and their applications.
method General construction of the Chern character map for twisted equivariant non-abelian cohomology.
result Illustrated the construction by computing the equivariant Sullivan model of Cohomotopy.
DeepCTRL integrates rules into deep learning models, allowing flexible control at inference.
problem Lack of flexibility in incorporating rules into deep learning models.
method Integrates rule representations into deep neural networks, enabling flexible control at inference.
result Improves rule verification ratio and accuracy gains at downstream tasks.
New methods prune unpromising rules from KGs, improving scalability and runtime.
problem Scalability issues in walk-based rule learning from KGs.
method Rule Hierarchy Framework (RHF) and Hierarchical Pruning (HPMs).
result Significant reductions in runtime and number of learned rules without compromising predictive performance.
Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distancesstat.ML
Paper analyzes convergence of ODE samplers in Wasserstein distances.
problem Limited theoretical understanding of convergence properties of probability flow ODEs.
method Convergence analysis for general probability flow ODEs in 2-Wasserstein distance.
result First non-asymptotic convergence analysis for probability flow ODE samplers.
Study defines new slant ruled surfaces in Minkowski 3-space.
problem Characterizing non-null ruled surfaces in Minkowski 3-space.
method Introduced new types of non-null ruled surfaces and their characterizations.
result Established relationships between non-null slant ruled surfaces and their striction lines.
Subcartesian spaces follow Leibniz' rule, simplifying differential calculus.
problem Simplifying differential calculus in subcartesian spaces.
method Showed derivations satisfy the chain rule and have maximal integral curves.
result Subcartesian spaces follow Leibniz' rule, simplifying differential calculus.
In this study, we give the relationships between the conical curvatures of ruled surfaces drawn by the unit vectors of the ruling, central normal and central tangent of a regular ruled surface in the Euclidean -space. We obtain the differential equations characterizing slant ruled surfaces and if the reference ruled su…
SEARNN improves RNN training by incorporating global-local losses.
problem RNNs trained with MLE fail to exploit structured losses and suffer from exposure bias.
method SEARNN introduces global-local losses through test-alike search space exploration.
result SEARNN outperforms MLE on OCR, spelling correction, and machine translation tasks.
New framework learns interpretable rule ensembles without sacrificing accuracy.
problem Trade-off between accuracy and interpretability in rule ensembles.
method Introduces local interpretability and a regularizer to promote it, using coordinate descent with local search.
result Learns rule ensembles with fewer rules to explain individual predictions, maintaining comparable accuracy.
R2N learns interpretable rules and literals from numerical features.
problem Lack of expressive vocabulary in rule-based decision models.
method Relational Rule Network (R2N) learns literals and rules end-to-end.
result Learned literals improve prediction accuracy and rule conciseness.
Study ruled surfaces with finite multiplicity, focusing on their curves and singularities.
problem Understanding ruled surfaces with finite multiplicity.
method Analyzing striction curves and singularities of ruled surfaces.
result Geometric meanings of invariants related to ruled surfaces.
All finite subdivision rules are combinatorially equivalent to three-dimensional subdivision rulesmath.GT
Finite subdivision rules in high dimensions are shown to be equivalent to 3D rules.
problem Visualization and construction of high-dimensional subdivision rules.
method Characterized history graphs and defined combinatorial subdivision rules.
result Finite subdivision rules in arbitrary dimensions are combinatorially equivalent to 3D rules.
Paper classifies ruled surfaces in Lorentz-Minkowski space for a specific flow.
problem Classifying ruled surfaces in Lorentz-Minkowski space.
method Examining homothetic self-similar solutions of the inverse mean curvature flow.
result Existence of two classes of non-cylindrical homothetic solitons.
A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunitycs.LG
Abstract: Maps fairness concepts to EOP, unifies them, and proposes new measures.
problem Understanding and interpreting fairness in machine learning.
method Mapping fairness concepts to EOP, formalizing existing fairness definitions, proposing new measures.
result Existing fairness definitions can be seen as special cases of EOP, and new measures proposed.
Slant Ruled Surfacesmath.DG
In this study, we define some new types of ruled surfaces called slant ruled surfaces. We give some characterizations for a regular ruled surface to be a slant ruled surface in Euclidean 3- space. We show that if the slant ruled surface is developable then the striction curve is a general helix or a slant helix accordi…
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
Subdivision rules create sequences of nested cell structures on CW-complexes, and they frequently arise from groups. In this paper, we develop several tools for classifying subdivision rules. We give a criterion for a subdivision rule to represent a Gromov hyperbolic space, and show that a subdivision rule for a hyperb…
In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are then obtained with logistic regression from the corresponding hard rules. In order to deal with the p…
Advances rule-based multi-label classification using conformal prediction.
problem Improving accuracy and decision making in multi-label classification.
method Combines conformal prediction with rule-based learning to provide natural conformity scores and calibrate rule assessments.
result Calibrated conformity scores enhance prediction accuracy and decision making.