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Found 20 papers, 10 papers with code
Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise
Despite the vast empirical evidence supporting the efficacy of adaptive optimization methods in deep learning, their theoretical understanding is far from complete.
LoFi: Neural Local Fields for Scalable Image Reconstruction
Neural fields or implicit neural representations (INRs) have attracted significant attention in computer vision and imaging due to their efficient coordinate-based representation of images and 3D volumes.
SeisLM: a Foundation Model for Seismic Waveforms
We introduce the Seismic Language Model (SeisLM), a foundational model designed to analyze seismic waveforms -- signals generated by Earth's vibrations such as the ones originating from earthquakes.
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability.
Direct Language Model Alignment from Online AI Feedback
Moreover, responses in these datasets are often sampled from a language model distinct from the one being aligned, and since the model evolves over training, the alignment phase is inevitably off-policy.
Decoding-time Realignment of Language Models
Aligning language models with human preferences is crucial for reducing errors and biases in these models.
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
Routers in Vision Mixture of Experts: An Empirical Study
Routers for sparse MoEs can be further grouped into two variants: Token Choice, which matches experts to each token, and Expert Choice, which matches tokens to each expert.
GLIMPSE: Generalized Local Imaging with MLPs
Deep learning is the current de facto state of the art in tomographic imaging.
Sparsity-Constrained Optimal Transport
The smoothness of the objectives increases as $k$ increases, giving rise to a trade-off between convergence speed and sparsity of the optimal plan.
Universal Approximation Under Constraints is Possible with Transformers
Many practical problems need the output of a machine learning model to satisfy a set of constraints, $K$.
Learning Multiscale Convolutional Dictionaries for Image Reconstruction
To close the performance gap, we thus propose a multiscale convolutional dictionary structure.
Finding trainable sparse networks through Neural Tangent Transfer
Deep neural networks have dramatically transformed machine learning, but their memory and energy demands are substantial.
Causally Denoise Word Embeddings Using Half-Sibling Regression
Distributional representations of words, also known as word vectors, have become crucial for modern natural language processing tasks due to their wide applications.
Harnessing Slow Dynamics in Neuromorphic Computation
We empirically show that, by harnessing slow dynamics, spiking neural networks on analog neuromorphic systems can gain non-trivial performance boosts on a battery of real-time signal processing tasks.
Continual Learning for Sentence Representations Using Conceptors
Distributed representations of sentences have become ubiquitous in natural language processing tasks.
Unsupervised Post-processing of Word Vectors via Conceptor Negation
Word vectors are at the core of many natural language processing tasks.
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 series models, model parameters are estimated based on trajectories of fully recorded observations.
Estimating Gradual-Emotional Behavior in One-Minute Videos with ESNs
In this paper, we describe our approach for the OMG- Emotion Challenge 2018.
Binary Compressive Sensing via Smoothed $\ell_0$ Gradient Descent
We present a Compressive Sensing algorithm for reconstructing binary signals from its linear measurements.
