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Found 29 papers, 17 papers with code
SpecRaGE: Robust and Generalizable Multi-view Spectral Representation Learning
We introduce $\textit{SpecRaGE}$, a novel fusion-based framework that integrates the strengths of graph Laplacian methods with the power of deep learning to overcome these challenges.
G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning
Most graph learning methods rely heavily on neighborhood information, raising the question of how to handle cold-start nodes - nodes with no known connections within the graph.
CoverBench: A Challenging Benchmark for Complex Claim Verification
We introduce CoverBench, a challenging benchmark focused on verifying LM outputs in complex reasoning settings.
Sparse Binarization for Fast Keyword Spotting
With the increasing prevalence of voice-activated devices and applications, keyword spotting (KWS) models enable users to interact with technology hands-free, enhancing convenience and accessibility in various contexts.
Multilingual Instruction Tuning With Just a Pinch of Multilinguality
As instruction-tuned large language models (LLMs) gain global adoption, their ability to follow instructions in multiple languages becomes increasingly crucial.
ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding
We introduce ZeroSCROLLS, a zero-shot benchmark for natural language understanding over long texts, which contains only test and small validation sets, without training data.
Causes and Cures for Interference in Multilingual Translation
Multilingual machine translation models can benefit from synergy between different language pairs, but also suffer from interference.
Efficient Long-Text Understanding with Short-Text Models
Transformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific articles and long documents, due to their quadratic complexity.
Ranked #6 on
Long-range modeling
on SCROLLS
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Instruction Induction: From Few Examples to Natural Language Task Descriptions
Large language models are able to perform a task by conditioning on a few input-output demonstrations - a paradigm known as in-context learning.
SCROLLS: Standardized CompaRison Over Long Language Sequences
NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild.
Ranked #8 on
Long-range modeling
on SCROLLS
Discovery of Single Independent Latent Variable
Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science.
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Experimenting on several real-world datasets, we demonstrate that our proposed approach outperforms similar approaches designed to avoid only correlated or nuisance features, but not both.
What Do You Get When You Cross Beam Search with Nucleus Sampling?
We combine beam search with the probabilistic pruning technique of nucleus sampling to create two deterministic nucleus search algorithms for natural language generation.
Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language
Current NLP datasets targeting ambiguity can be solved by a native speaker with relative ease.
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities
We empirically analyze the different components of our proposed approach and demonstrate their contribution to the performance of the model.
Ranked #8 on
Age And Gender Classification
on Adience Age
Neural Machine Translation without Embeddings
Many NLP models operate over sequences of subword tokens produced by hand-crafted tokenization rules and heuristic subword induction algorithms.
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
In this paper, we present a method for unsupervised feature selection, and we demonstrate its use for the task of clustering.
Learning to Ask Medical Questions using Reinforcement Learning
We propose a novel reinforcement learning-based approach for adaptive and iterative feature selection.
Defending against Adversarial Images using Basis Functions Transformations
We study the effectiveness of various approaches that defend against adversarial attacks on deep networks via manipulations based on basis function representations of images.
SpectralNet: Spectral Clustering using Deep Neural Networks
Moreover, the map learned by SpectralNet naturally generalizes the spectral embedding to unseen data points.
Stochastic Neighbor Embedding separates well-separated clusters
Stochastic Neighbor Embedding and its variants are widely used dimensionality reduction techniques -- despite their popularity, no theoretical results are known.
Removal of Batch Effects using Distribution-Matching Residual Networks
We apply our method to mass cytometry and single-cell RNA-seq datasets, and demonstrate that it effectively attenuates batch effects.
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations.
A Deep Learning Approach to Unsupervised Ensemble Learning
We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning.
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks
We consider the statistical problem of learning common source of variability in data which are synchronously captured by multiple sensors, and demonstrate that Siamese neural networks can be naturally applied to this problem.
Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization
We propose a general framework for increasing local stability of Artificial Neural Nets (ANNs) using Robust Optimization (RO).
Provable approximation properties for deep neural networks
We discuss approximation of functions using deep neural nets.
Diffusion Nets
In this paper, we propose a manifold learning algorithm based on deep learning to create an encoder, which maps a high-dimensional dataset and its low-dimensional embedding, and a decoder, which takes the embedded data back to the high-dimensional space.
