Search Results for author:
Found 8 papers, 2 papers with code
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture
We ask: are there performant architectures that can scale sub-quadratically along sequence length and model dimension?
Accelerating LLM Inference with Staged Speculative Decoding
Recent advances with large language models (LLM) illustrate their diverse capabilities.
Exhaustivity and anti-exhaustivity in the RSA framework: Testing the effect of prior beliefs
During communication, the interpretation of utterances is sensitive to a listener's probabilistic prior beliefs, something which is captured by one currently influential model of pragmatics, the Rational Speech Act (RSA) framework.
Bounding the Last Mile: Efficient Learned String Indexing
RSS achieves this by using the minimal string prefix to sufficiently distinguish the data unlike most learned approaches which index the entire string.
On the Optimality of Vagueness: "Around", "Between", and the Gricean Maxims
Focusing on expressions of approximation such as "around", which are semantically vague, we show that they allow the speaker to convey indirect probabilistic information, in a way that can give the listener a more accurate representation of the information available to the speaker than any more precise expression would (intervals of the form "between").
Preventing Adversarial Use of Datasets through Fair Core-Set Construction
We propose improving the privacy properties of a dataset by publishing only a strategically chosen "core-set" of the data containing a subset of the instances.
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors
Recent work in deep reinforcement learning has allowed algorithms to learn complex tasks such as Atari 2600 games just from the reward provided by the game, but these algorithms presently require millions of training steps in order to learn, making them approximately five orders of magnitude slower than humans.
