4 citations · 8 across the 20 of their papers we have counts for
14 papers · 1 filter
KV-streams for Efficient Compaction in Agentic Reinforcement Learning
Emiliano Penaloza, Dane Malenfant, Dheeraj Vattikonda +15
Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most popular mechanism to alleviate…
Can In-Context Learning Support Intrinsic Curiosity?
Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald +5
Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the pro…
Iterative Amortized Inference: Unifying In-Context Learning and Learned Optimizers
Sarthak Mittal, Divyat Mahajan, Guillaume Lajoie +1
Modern learning systems increasingly rely on amortized learning - the idea of reusing computation or inductive biases shared across tasks to enable rapid generalization to novel pr…
Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, Vineet Jain, Sarthak Mittal +9
Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference to make a prediction. Inference-time co…
Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective
Leo Gagnon, Eric Elmoznino, Sarthak Mittal +4
The rapid adaptation ability of auto-regressive foundation models is often attributed to the diversity of their pre-training data. This is because, from a Bayesian standpoint, mini…
MesaNet: Sequence Modeling by Locally Optimal Test-Time Training
Johannes von Oswald, Nino Scherrer, Seijin Kobayashi +14
Sequence modeling is currently dominated by causal transformer architectures that use softmax self-attention. Although widely adopted, transformers require scaling memory and compu…
In-Context Parametric Inference: Point or Distribution Estimators?
Sarthak Mittal, Yoshua Bengio, Nikolay Malkin +1
Bayesian and frequentist inference are two fundamental paradigms in statistical estimation. Bayesian methods treat hypotheses as random variables, incorporating priors and updating…
Amortized In-Context Bayesian Posterior Estimation
Sarthak Mittal, Niels Leif Bracher, Guillaume Lajoie +2
Bayesian inference provides a natural way of incorporating prior beliefs and assigning a probability measure to the space of hypotheses. Current solutions rely on iterative routine…
In-context learning and Occam's razor
Eric Elmoznino, Tom Marty, Tejas Kasetty +5
A central goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees for generalization without further assumpt…
Latent Representation Learning for Multimodal Brain Activity Translation
Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch +5
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high tempora…
When can transformers compositionally generalize in-context?
Seijin Kobayashi, Simon Schug, Yassir Akram +5
Many tasks can be composed from a few independent components. This gives rise to a combinatorial explosion of possible tasks, only some of which might be encountered during trainin…
Does learning the right latent variables necessarily improve in-context learning?
Sarthak Mittal, Eric Elmoznino, Leo Gagnon +4
Large autoregressive models like Transformers can solve tasks through in-context learning (ICL) without learning new weights, suggesting avenues for efficiently solving new tasks.…
Amortizing intractable inference in large language models
Edward J. Hu, Moksh Jain, Eric Elmoznino +4
Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits tractable querying of this knowled…
Delta-AI: Local objectives for amortized inference in sparse graphical models
Jean-Pierre Falet, Hae Beom Lee, Esmeralda S. Whitammer +6
We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call -amortized inference (-AI). Our approach is based on the obs…