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Found 14 papers, 4 papers with code
Fewer Truncations Improve Language Modeling
In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens.
Taxonomy Expansion for Named Entity Recognition
We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0. 5 - 2. 5 F1), including in novel settings for taxonomy expansion not considered in prior work.
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks.
À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting
During inference, models can be assembled based on arbitrary selections of data sources, which we call "\`a-la-carte learning".
A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting
During inference, models can be assembled based on arbitrary selections of data sources, which we call a-la-carte learning.
DIVA: Dataset Derivative of a Learning Task
A learning task is a function from a training set to the validation error, which can be represented by a trained deep neural network (DNN).
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection
After modeling the signals, we use an anomaly detection system based on the classic CUMSUM algorithm and a variational approximation of the $f$-divergence to detect both isolated point anomalies and change-points in statistics of the signals.
Factoring isometries of quadratic spaces into reflections
In particular, we show that an isometry is a product of positive reflections if and only if its spinor norm is positive.
Group Theory
Estimating informativeness of samples with Smooth Unique Information
We define a notion of information that an individual sample provides to the training of a neural network, and we specialize it to measure both how much a sample informs the final weights and how much it informs the function computed by the weights.
Structured Prediction as Translation between Augmented Natural Languages
We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.
Ranked #3 on
Relation Classification
on TACRED
Where is the Information in a Deep Neural Network?
We establish a novel relation between the information in the weights and the effective information in the activations, and use this result to show that models with low (information) complexity not only generalize better, but are bound to learn invariant representations of future inputs.
The Information Complexity of Learning Tasks, their Structure and their Distance
Our framework is the first to measure complexity in a way that accounts for the effect of the optimization scheme, which is critical in Deep Learning.
Impossibility results on stability of phylogenetic consensus methods
We answer two questions raised by Bryant, Francis and Steel in their work on consensus methods in phylogenetics.
On the local homology of Artin groups of finite and affine type
In all finite and affine cases, we are able to construct Morse matchings of a special type (we call them "precise matchings").
Algebraic Topology Combinatorics Group Theory
