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Found 10 papers, 3 papers with code
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network.
Ranked #4 on
Classification
on InDL
Motion Prediction Under Multimodality with Conditional Stochastic Networks
In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables.
Watch Your Step: Learning Node Embeddings via Graph Attention
Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e. g. by sampling random walks).
Ranked #69 on
Node Classification
on Citeseer
TensorFlow Distributions
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.
An information-theoretic analysis of deep latent-variable models
We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference.
Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces
Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information.
Small-scale proxies for large-scale Transformer training instabilities
In this work, we seek ways to reproduce and study training stability and instability at smaller scales.
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?
We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment.
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST$^{EM}$, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times.
Training LLMs over Neurally Compressed Text
In this paper, we explore the idea of training large language models (LLMs) over highly compressed text.
