
Universal Approximation Under Constraints is Possible with Transformers
Many practical problems need the output of a machine learning model to s...
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Interpreting UNets via TaskDriven Multiscale Dictionary Learning
UNets have been tremendously successful in many imaging inverse problem...
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Finding trainable sparse networks through Neural Tangent Transfer
Deep neural networks have dramatically transformed machine learning, but...
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Causally Denoise Word Embeddings Using HalfSibling Regression
Distributional representations of words, also known as word vectors, hav...
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Harnessing Slow Dynamics in Neuromorphic Computation
Neuromorphic Computing is a nascent research field in which models and d...
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Continual Learning for Sentence Representations Using Conceptors
Distributed representations of sentences have become ubiquitous in natur...
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Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors
Distributed representations of words, better known as word embeddings, h...
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Unsupervised Postprocessing of Word Vectors via Conceptor Negation
Word vectors are at the core of many natural language processing tasks. ...
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A Consistent Method for Learning OOMs from Asymptotically Stationary Time Series Data Containing Missing Values
In the traditional framework of spectral learning of stochastic time ser...
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Fast Binary Compressive Sensing via ℓ_0 Gradient Descent
We present a fast Compressive Sensing algorithm for the reconstruction o...
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Tianlin Liu
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