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Found 21 papers, 10 papers with code
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Recently, several attempts have been made to replace such domain-specific, human-designed transformations with generated views that are learned.
Model Stitching: Looking For Functional Similarity Between Representations
Model stitching (Lenc & Vedaldi 2015) is a compelling methodology to compare different neural network representations, because it allows us to measure to what degree they may be interchanged.
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
In this work, we present Multi-Symmetry Ensembles (MSE), a framework for constructing diverse ensembles by capturing the multiplicity of hypotheses along symmetry axes, which explore the hypothesis space beyond stochastic perturbations of model weights and hyperparameters.
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows
Studying the dynamics of open quantum systems can enable breakthroughs both in fundamental physics and applications to quantum engineering and quantum computation.
QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
Quantum optimization, a key application of quantum computing, has traditionally been stymied by the linearly increasing complexity of gradient calculations with an increasing number of parameters.
On the Importance of Calibration in Semi-supervised Learning
State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been highly successful in leveraging a mix of labeled and unlabeled data by combining techniques of consistency regularization and pseudo-labeling.
AI-Assisted Discovery of Quantitative and Formal Models in Social Science
In social science, formal and quantitative models, such as ones describing economic growth and collective action, are used to formulate mechanistic explanations, provide predictions, and uncover questions about observed phenomena.
Discovering Conservation Laws using Optimal Transport and Manifold Learning
We test this new approach on a variety of physical systems and demonstrate that our method is able to both identify the number of conserved quantities and extract their values.
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings.
Ranked #13 on
Semantic Textual Similarity
on STS16
Meta-Learning and Self-Supervised Pretraining for Real World Image Translation
In recent years, emerging fields such as meta-learning or self-supervised learning have been closing the gap between proof-of-concept results and real-life applications of machine learning by extending deep-learning to the semi-supervised and few-shot domains.
Equivariant Contrastive Learning
In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science
Deep learning techniques have been increasingly applied to the natural sciences, e. g., for property prediction and optimization or material discovery.
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations
In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
The attention mechanism is a key component of the neural revolution in Natural Language Processing (NLP).
Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in NLP Applications
To address this issue, we propose a novel vector-vector-matrix architecture (VVMA), which greatly reduces the latency at inference time for NMT.
On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning
We believe that our rethinking of the Wasserstein-Procrustes problem could enable further research, thus helping to develop better algorithms for aligning word embeddings across languages.
Contextualizing Enhances Gradient Based Meta Learning
We propose the implementation of contextualizers, which are generalizable prototypes that adapt to given examples and play a larger role in classification for gradient-based models.
Fast Neural Models for Symbolic Regression at Scale
Deep learning owes much of its success to the astonishing expressiveness of neural networks.
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications
Stacking long short-term memory (LSTM) cells or gated recurrent units (GRUs) as part of a recurrent neural network (RNN) has become a standard approach to solving a number of tasks ranging from language modeling to text summarization.
WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices
We present a logarithmic-scale efficient convolutional neural network architecture for edge devices, named WaveletNet.
Rotational Unit of Memory
We evaluate our model on synthetic memorization, question answering and language modeling tasks.
Ranked #5 on
Question Answering
on bAbi
(Accuracy (trained on 1k) metric)
