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Found 9 papers, 4 papers with code
How Lexical is Bilingual Lexicon Induction?
In contemporary machine learning approaches to bilingual lexicon induction (BLI), a model learns a mapping between the embedding spaces of a language pair.
Inverse distance weighting attention
We report the effects of replacing the scaled dot-product (within softmax) attention with the negative-log of Euclidean distance.
The Kernel Density Integral Transformation
We demonstrate that, without hyperparameter tuning, the kernel density integral transformation can be used as a simple drop-in replacement for either method, offering protection from the weaknesses of each.
Look-ups are not (yet) all you need for deep learning inference
Fast approximations to matrix multiplication have the potential to dramatically reduce the cost of neural network inference.
Adaptive Block Floating-Point for Analog Deep Learning Hardware
Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts.
Towards Backwards-Compatible Data with Confounded Domain Adaptation
Domain adaptation approaches which do account for such confounding are designed to adapt covariates to optimally predict a particular label whose shift is confounded with covariate shift.
If Loud Aliens Explain Human Earliness, Quiet Aliens Are Also Rare
We fit this three-parameter model of loud aliens to data: 1) birth power from the number of hard steps seen in Earth history, 2) birth constant by assuming a inform distribution over our rank among loud alien birth dates, and 3) expansion speed from our not seeing alien volumes in our sky.
Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models
While highly scalable optimization methods exist for sparse Gaussian graphical model estimation, state-of-the-art methods for conditional Gaussian graphical models are not efficient enough and more importantly, fail due to memory constraints for very large problems.
On Sparse Gaussian Chain Graph Models
In this paper, we address the problem of learning the structure of Gaussian chain graph models in a high-dimensional space.
