5,727 research outputs found
AudioLM: a Language Modeling Approach to Audio Generation
We introduce AudioLM, a framework for high-quality audio generation with
long-term consistency. AudioLM maps the input audio to a sequence of discrete
tokens and casts audio generation as a language modeling task in this
representation space. We show how existing audio tokenizers provide different
trade-offs between reconstruction quality and long-term structure, and we
propose a hybrid tokenization scheme to achieve both objectives. Namely, we
leverage the discretized activations of a masked language model pre-trained on
audio to capture long-term structure and the discrete codes produced by a
neural audio codec to achieve high-quality synthesis. By training on large
corpora of raw audio waveforms, AudioLM learns to generate natural and coherent
continuations given short prompts. When trained on speech, and without any
transcript or annotation, AudioLM generates syntactically and semantically
plausible speech continuations while also maintaining speaker identity and
prosody for unseen speakers. Furthermore, we demonstrate how our approach
extends beyond speech by generating coherent piano music continuations, despite
being trained without any symbolic representation of music
Pushdown Control-Flow Analysis for Free
Traditional control-flow analysis (CFA) for higher-order languages, whether
implemented by constraint-solving or abstract interpretation, introduces
spurious connections between callers and callees. Two distinct invocations of a
function will necessarily pollute one another's return-flow. Recently, three
distinct approaches have been published which provide perfect call-stack
precision in a computable manner: CFA2, PDCFA, and AAC. Unfortunately, CFA2 and
PDCFA are difficult to implement and require significant engineering effort.
Furthermore, all three are computationally expensive; for a monovariant
analysis, CFA2 is in , PDCFA is in , and AAC is in .
In this paper, we describe a new technique that builds on these but is both
straightforward to implement and computationally inexpensive. The crucial
insight is an unusual state-dependent allocation strategy for the addresses of
continuation. Our technique imposes only a constant-factor overhead on the
underlying analysis and, with monovariance, costs only O(n3) in the worst case.
This paper presents the intuitions behind this development, a proof of the
precision of this analysis, and benchmarks demonstrating its efficacy.Comment: in Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on
Principles of Programming Languages, 201
A uniform definition of stochastic process calculi
We introduce a unifying framework to provide the semantics of process algebras, including their quantitative variants useful for modeling quantitative aspects of behaviors. The unifying framework is then used to describe some of the most representative stochastic process algebras. This
provides a general and clear support for an understanding of their similarities and differences. The framework is based on State to Function Labeled Transition Systems, FuTSs for short, that are state-transition structures where each transition is a triple of the form (s; Ī±;P). The first andthe second components are the source state, s, and the label, Ī±, of the transition, while the third component is the continuation function, P, associating a value of a suitable type to each state s0. For example, in the case of stochastic process algebras the value of the continuation function on s0 represents the rate of the negative exponential distribution characterizing the duration/delay of the action performed to reach state s0 from s. We first provide the semantics of a simple formalism used to describe Continuous-Time Markov Chains, then we model a number of process algebras that permit parallel composition of models according to the two main interaction paradigms (multiparty and one-to-one synchronization). Finally, we deal with formalisms where actions and rates are kept separate and address the issues related to the coexistence of stochastic, probabilistic, and non-deterministic behaviors. For each formalism, we establish the formal correspondence between the FuTSs semantics and its original semantics
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