Estimates watermarked content proportions in mixed-source texts.
problem Optimally estimating the proportion of watermarked content in texts with mixed sources.
method Casting the problem as estimating a proportion parameter in a mixture model based on pivotal statistics.
result Proposes efficient estimators for watermark proportion and shows their accuracy through evaluations.
WISER detects watermarked segments in text via epidemic change-point analysis.
problem Identifying watermarked segments in mixed-source texts.
method Epidemic change-point perspective, WISER algorithm.
result WISER outperforms state-of-the-art methods in accuracy and speed.
This paper proposes an alternative algorithm for multichannel variational autoencoder (MVAE), a recently proposed multichannel source separation approach. While MVAE is notable in its impressive source separation performance, the convergence-guaranteed optimization algorithm and that it allows us to estimate source-cla…
Study on reliability of latent reuse in diffusion models under distribution shift.
problem When can latent spaces from a source dataset be reused for a target dataset with different distributions?
method Considered a source-target setting with approximately low-dimensional datasets near different subspaces. Analyzed the target-domain score error due to principal-angle misalignment and target ambient noise.
result Latent reuse is reliable only if the source and target subspaces are close and the target ambient noise is not too amplified.
Improves unsupervised domain adaptation by mixing source and target domains.
problem Improves unsupervised domain adaptation by mixing source and target domains.
method Enforces training constraints across domains using mixup formulation and feature-level consistency regularizer.
result Significantly improves state-of-the-art performance on image classification and human activity recognition tasks.
DMRL improves UDA by mixing source and target samples and enriching latent space structures.
problem Lack of class-aware information and insufficient samples for domain-invariant feature extraction.
method Dual Mixup Regularized Learning (DMRL) that conducts category and domain mixup regularizations.
result DMRL achieves state-of-the-art performance on domain adaptation benchmarks.
For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains unclear why these linear methods work well with real-world data that involve nonlinear source mixtures. This work theoretically validates that…
Scene text magnifier aims to magnify text in natural scene images without recognition. It could help the special groups, who have myopia or dyslexia to better understand the scene. In this paper, we design the scene text magnifier through interacted four CNN-based networks: character erasing, character extraction, char…
The paper classifies actions of a specific group on certain manifolds.
problem Classifying analytic actions of a specific semi-orthogonal group on manifolds.
method Adapting Uchida's construction, the paper explicitly constructs actions on specific manifolds and demonstrates that any action is covered by these.
result Any analytic action of the semi-orthogonal group on a closed, connected manifold is covered by the constructed actions.
W-RNN improves text classification by extracting serialized text semantics.
problem Semantic constraint in sparse representation classification methods.
method Weighted RNN using word vectors and recurrent neural networks.
result W-RNN outperforms other methods in precision, recall, F1, and loss values.
The paper solves the Nielsen realization problem for high degree del Pezzo surfaces.
problem Which finite subgroups of the mapping class group of a del Pezzo surface lift to the diffeomorphism group?
method Classification and partial answers for d≥7, equivariant connected sum for d=6. result Complete classification for d≥7, partial answer for d=6. We establish a gluing construction for Higgs bundles over a connected sum of Riemann surfaces in terms of solutions to the Sp(4,R)-Hitchin equations using the linearization of a relevant elliptic operator. The construction can be used to provide model Higgs bundles in all the 2g−3 exce…
A new text representation model combines CNN and VAE for better semantic extraction.
problem Difficult to effectively extract semantic features and distinguish polysemy in text data.
method Integrates CNN for feature extraction and VAE for consistent Gaussian distribution.
result The model outperforms traditional classification algorithms in text classification tasks.
We propose two algorithms that can find local minima faster than the state-of-the-art algorithms in both finite-sum and general stochastic nonconvex optimization. At the core of the proposed algorithms is One-epoch-SNVRG+ using stochastic nested variance reduction (Zhou et al., 2018a), which outperforms the s…
New families of Lie groups with special foliations discovered.
problem Characterizing left-invariant foliations on semi-Riemannian Lie groups.
method Classifying foliations generated by specific subgroups.
result Constructing new families of Lie groups with conformal minimal foliations.
Improved text summarization using belief propagation on weighted bipartite graphs.
problem Text summarization from a graph theory perspective.
method Generalized belief propagation algorithm for weighted bipartite graphs.
result Our algorithm outperforms greedy methods in text summarization tasks.
New TC variant dTC better fits motion planning for some systems.
problem Improving motion planning for autonomous systems.
method Defined and computed new homotopy invariant dTC.
result dTC and dcat provide better motion planning solutions.
Let Man∗ denote the category of closed, connected, oriented and based 3-manifolds, with basepoint preserving diffeomorphisms between them. Juhász, Thurston and Zemke showed that the Heegaard Floer invariants are natural with respect to diffeomorphisms, in the sense that there are functors $HF^{\circ}: \te…
StoryGen uses images to generate coherent text from multiple images.
problem Generating coherent text from multiple images.
method Designing a Relational Text Data Generator algorithm to relate features from plural images.
result The model can generate meaningful paragraphs containing extracted features from input images.
Paper generates diverse, readable adversarial texts from scratch.
problem Text classification models are easily fooled by adversarial examples.
method Trained a conditional variational autoencoder (VAE) with adversarial loss and utilized GANs to generate consistent adversarial texts.
result Successfully generates adversarial texts with higher success rate and acceptable quality.
Unified framework improves NLP tasks by converting diverse problems into text-to-text format.
problem Improving natural language processing tasks through transfer learning.
method Unified text-to-text transformer framework, comparing various pre-training objectives and architectures.
result Achieved state-of-the-art results on multiple NLP benchmarks.
SCROLLS benchmarks long text NLP tasks, improving existing models.
problem Short NLP benchmarks ignore long texts; SCROLLS addresses this.
method Handpicked long-text datasets for summarization, QA, and inference tasks.
result Improvement potential on SCROLLS tasks, as indicated by initial baselines.
New method detects text changes under dependencies, outperforming baselines.
problem Detecting structural changes in m-dependent text data. method Kernel change-point detection under m-dependence. result Consistent and weakly consistent detection of change points in m-dependent text. Contextual bandits study how reward variance affects regret bounds.
problem Investigating how small reward variance impacts regret bounds in contextual bandits.
method Analyzing two types of adversaries and function approximation complexities.
result Regret bounds are influenced by the eluder dimension and reward variance.
The paper studies symplectic structures on character varieties of Sasakian threefolds.
problem Character varieties of Sasakian threefolds and their symplectic structures.
method Constructing a natural algebraic 2-form and showing its properties.
result The restriction of the 2-form to the space of irreducible SU(r) homomorphisms is symplectic.
Proposes spherical text embedding for better directional similarity.
problem Directional similarity is more effective but unsupervised text embeddings are typically learned in Euclidean space.
method Develops a spherical generative model and an efficient optimization algorithm for unsupervised word and paragraph embeddings.
result Achieves state-of-the-art performances on various text embedding tasks.
Recent years have seen remarkable progress of text generation in different contexts, such as the most common setting of generating text from scratch, and the emerging paradigm of retrieval-and-rewriting. Text infilling, which fills missing text portions of a sentence or paragraph, is also of numerous use in real life, …
We study the behavior of the second order Renormalization Group flow on locally homogeneous metrics on closed three-manifolds. In the cases R3 and SO(3)×R, the flow is qualitatively the same as the Ricci flow. In the cases H(3) and H(2)×R, if the curvature is small, t…
A graph model improves short text classification by integrating sentence relationships.
problem Sparse features in short text classification due to limited text length.
method PathWalk model combining graph networks and short sentences.
result PathWalk achieves state-of-the-art results on four datasets.
Joyce showed that for a classical knot K, the involutory medial quandle IMQ(K) is isomorphic to the core quandle of the homology group H1(X2), where X2 is the cyclic double cover of S3, branched over K. It follows that ∣IMQ(K)∣=∣detK∣. In the present paper, the extension o…
TextNAS finds optimal text representation networks using neural architecture search.
problem Finding the optimal text representation networks is challenging.
method Proposes a novel search space for text representation and uses automatic neural architecture search.
result Automatic search discovers network architectures that outperform state-of-the-art models on text classification and natural language inference tasks.
Paper proposes ICCN to learn correlations between text, audio, and video for multimodal sentiment analysis.
problem Improving multimodal sentiment analysis by learning hidden correlations between text and audio/video features.
method Interaction Canonical Correlation Network (ICCN) using deep canonical correlation analysis (DCCA).
result Empirical results confirm the effectiveness of ICCN in capturing useful information from all three views.
The paper classifies nilmanifolds with specific SL(3,C) structures.
problem Classifying nilmanifolds with invariant mean convex or tamed SL(3,C) structures.
method Invariants and classification of structures on nilmanifolds.
result Classification of nilmanifolds with invariant mean convex closed SL(3,C) structures.
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.
Proposes a flexible neural recommendation framework for better prediction performance.
problem Data sparsity, cold start problem, and long-tail distribution in recommendations.
method A modular neural recommendation framework that includes a neural collaborative filtering part and a text processing part as a regularizer.
result Achieves better prediction performance than state-of-the-art text-aware methods using a simple text processing approach.
Let G(n) be equal either to PO(n,1),PU(n,1) or PSp(n,1) and let Γ≤G(n) be a uniform lattice. Denote by HKn the hyperbolic space associated to G(n), where K is a division algebra over the reals of dimension d=dimRK. Assume $d(n-1) \ge…
A new framework uses text descriptions to improve protein design.
problem Lack of effective methods to incorporate textual descriptions in protein design.
method ProteinDT framework that combines text and protein structural information.
result ProteinDT significantly improves protein design accuracy and performance.
MoleculeSTM learns from molecule structures and texts for better drug design.
problem Lack of integration between chemical structures and textual knowledge in AI drug discovery.
method Jointly learns chemical structures and texts via contrastive learning, using a large dataset.
result MoleculeSTM achieves state-of-the-art performance in zero-shot tasks like structure-text retrieval and molecule editing.
Survey of deep learning for Hindi text classification.
problem Limited research on morphologically rich, low-resource Hindi text classification.
method Comparison of CNN, LSTM, Transformer, BERT, and LASER for Hindi text classification.
result Multilingual pre-trained sentence embeddings outperform traditional architectures for Hindi text classification.
Unified causal model improves controllable text generation without bias.
problem Controllable text generation tasks, biased by prior models.
method Unified causal framework for attribute-conditional generation and text attribute transfer.
result Significant superiority over previous conditional models for improved control and reduced bias.
Image captioning has demonstrated models that are capable of generating plausible text given input images or videos. Further, recent work in image generation has shown significant improvements in image quality when text is used as a prior. Our work ties these concepts together by creating an architecture that can enabl…
TBIP uses texts to quantify lawmakers' political positions.
problem Quantifying lawmakers' political positions from speeches, tweets, etc.
method Unsupervised probabilistic topic model analyzing texts.
result TBIP separates lawmakers by party and infers ideal points close to vote-based.
Bayesian Topic Regression models causal inference with text and numerical data.
problem Causal inference using observational text data with both text and numerical confounders.
method Combines supervised Bayesian topic model with Bayesian regression framework, respecting the Frisch-Waugh-Lovell theorem.
result Joint approach recovers ground truth with lower bias than benchmarks, superior prediction results compared to separate approaches.
New coarse LS-category introduced for groups and spaces.
problem Large-scale topological properties of groups and spaces.
method Introducing a coarse analog of Lusternik-Schnirelmann category for metric spaces.
result Established lower and upper bounds for geometrically finite and bicombable groups.
SpeakerStew verifies 46 languages with reduced training and inference costs.
problem Speaker verification for 46 languages with smart speaker interactions.
method Pooling multilingual data, triage between text-dependent and text-independent models.
result Training on multiple languages generalizes well and reduces computational requirements.
In this work, we study abstractive text summarization by exploring different models such as LSTM-encoder-decoder with attention, pointer-generator networks, coverage mechanisms, and transformers. Upon extensive and careful hyperparameter tuning we compare the proposed architectures against each other for the abstractiv…
Universal perturbations misclassify text with high accuracy.
problem Vulnerability of text classifiers to small perturbations.
method Algorithm to compute universal adversarial perturbations.
result Deep neural networks are highly vulnerable to universal adversarial perturbations.
Single-stage neural architecture improves text-to-image synthesis.
problem Combining text and image generation is challenging.
method Used deep residual networks and sentence interpolation.
result Achieved state-of-the-art performance with single-stage training.