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Found 14 papers, 7 papers with code
AI-generated text boundary detection with RoFT
Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated.
Disentanglement Learning via Topology
We propose TopDis (Topological Disentanglement), a method for learning disentangled representations via adding multi-scale topological loss term.
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
Rapidly increasing quality of AI-generated content makes it difficult to distinguish between human and AI-generated texts, which may lead to undesirable consequences for society.
Learning Topology-Preserving Data Representations
The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topological features (clusters, loops, 2D voids, etc.)
Topological Data Analysis for Speech Processing
We apply topological data analysis (TDA) to speech classification problems and to the introspection of a pretrained speech model, HuBERT.
Acceptability Judgements via Examining the Topology of Attention Maps
The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP.
Ranked #1 on
Linguistic Acceptability
on ItaCoLA
Representation Topology Divergence: A Method for Comparing Neural Network Representations
Comparison of data representations is a complex multi-aspect problem that has not enjoyed a complete solution yet.
Representation Topology Divergence: A Method for Comparing Neural Network Representations.
We propose a method for comparing two data representations.
Artificial Text Detection via Examining the Topology of Attention Maps
The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content.
Manifold Topology Divergence: a Framework for Comparing Data Manifolds
We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models.
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.
We propose a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models.
Topological obstructions in neural networks learning
We define the neural network Topological Obstructions score, "TO-score", with the help of robust topological invariants, barcodes of the loss function, that quantify the "badness" of local minima for gradient-based optimization.
Barcodes as summary of objective function's topology
We apply the canonical forms (barcodes) of gradient Morse complexes to explore topology of loss surfaces.
Barcodes as summary of objective functions' topology
We apply canonical forms of gradient complexes (barcodes) to explore neural networks loss surfaces.
