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Found 14 papers, 4 papers with code
Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler
This is not only because there is a complicated correlation between learning rate, batch size, number of training tokens, model size, and other hyperparameters but also because it is prohibitively expensive to perform a hyperparameter search for large language models with Billions or Trillions of parameters.
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously.
LAB: Large-Scale Alignment for ChatBots
This work introduces LAB (Large-scale Alignment for chatBots), a novel methodology designed to overcome the scalability challenges in the instruction-tuning phase of large language model (LLM) training.
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception
Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems.
Object-Centric Diagnosis of Visual Reasoning
In contrast, symbolic and modular models have a relatively better grounding and robustness, though at the cost of accuracy.
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
First, we generate images in low-frequency bands by training a sampler in the wavelet domain.
CZ-GEM: A FRAMEWORK FOR DISENTANGLED REPRESENTATION LEARNING
In this work, we tackle a slightly more intricate scenario where the observations are generated from a conditional distribution of some known control variate and some latent noise variate.
SimVAE: Simulator-Assisted Training forInterpretable Generative Models
Then an inference network (encoder)is trained to invert the decoder.
Minnorm training: an algorithm for training over-parameterized deep neural networks
To solve this constrained optimization problem, our method employs Lagrange multipliers that act as integrators of error over training and identify `support vector'-like examples.
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework
A central challenge in sensory neuroscience is describing how the activity of populations of neurons can represent useful features of the external environment.
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization.
Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be?
This paper also introduces a new ensemble construction variant that combines hyperparameter optimization with the construction of ensembles.
Facial Expression Recognition
Facial Expression Recognition (FER)
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