21 papers · ranked by Valyu relevance
Yu Yu, Zhihong Sun, Jia Li, Yao Wan + 7 more
Large Language Models (LLMs) are capable of generating syntactically correct and functionally complete programs, greatly streamlining software development. However, recent studies reveal that these programs typically execute substantially slower than human-optimized counterparts. Existing approaches to bridging this…
Yu Yu, Chen Lyu
With the remarkable progress of Code Large Language Models (Code LLMs) in achieving semantic correctness, execution efficiency has become an increasingly important dimension for evaluating their practical utility. However, existing approaches typically treat full programs as a single optimization target during…
Kyle Bojanek, Olivier Marre, Stephanie E. Palmer
Populations of sensory neurons are thought to be shaped by selective pressures for optimal information transmission, yet real neural circuits display substantial variability across stimulus repeats, across time, and between individuals. Reconciling this variability with normative theories requires understanding not…
Kees Schouhamer Immink, Jos H. Weber, Tuan Thanh Nguyen, Kui Cai + 2 more
The design of low-complexity and efficient constrained codes has been a major research item for many years. This paper reports on a versatile method named concatenated constrained codes for designing efficient fixed-length constrained codes with small complexity. A concatenated constrained code comprises two (or more)…
Arthur Prat-Carrabin, Maximilian V. Harl, Samuel J. Gershman
As the statistics of sensory environments often change, neural sensory systems must adapt to maintain useful representations. Efficient coding prescribes that neuronal tuning curves should be optimized to the prior, but whether they can adapt rapidly is unclear. Empirically, tuning curves after repeated stimulus…
Saurabh Bedi, Gilles de Hollander, Maximilian Harl, Christian C. Ruff
To act effectively, the brain must transform information through a chain of processing stages, from sensing the environment, to evaluating options, to selecting actions. Because neural resources are limited, each stage should represent information efficiently. Yet efficient coding has been studied almost exclusively in…
William Dorrell, Peter E. Latham, Timothy E. J. Behrens, James C. R. Whittington
The efficient coding hypothesis presents a compelling success story for theoretical and systems neuroscience. It marshals a unifying idea, that neural codes can be understood as efficient encodings of natural stimuli, to explain phenomena from across sensory systems, sometimes with exquisite precision. However, similar…
Yuhang Wang, Weihua Chen, Linjing Song, Zhiping Xu + 6 more
With the rapid growth of data volume in sensor networks, lossy source coding systems achieve high-efficiency data compression with low distortion under limited transmission bandwidth. However, conventional compression algorithms rely on a two-stage framework with high computational complexity and frequently struggle to…
Jirui Liu, Longsheng Jiang, Xueting Li, Jiamin Wu + 1 more
Efficient coding is essential for sensory systems to extract meaningful information from the environment. Here, we investigate how stimulus-driven thermodynamic shifts and geometric reorganization enable efficient population coding. Using wide-field calcium imaging, we simultaneously recorded neuronal activity across…
Hyeyoung Shin
Perception is a process of inference, whereby incoming sensory evidence is interpreted based on prior expectations about the sensory world. Thus, the neural code of perception should be evaluated based on how optimally it computes perceptual inference. However, the neural code of perception has conventionally been…
Karim G. Habashy, Benjamin D. Evans, Dan F. M. Goodman, Jeffrey S. Bowers
The genomic mechanisms that efficiently encode the initial architecture and synaptic connectivity of neural circuits remain poorly understood. We hypothesise that two primary mechanisms — spatial encoding and factorisation — enable a limited genome to initialise networks of billions of neurons. Spatial encoding, a form…
Ying Chen, Jianrong Bao, Yanhai Shang, Chao Liu + 5 more
To obtain efficient channel codes with high power efficiency at moderate signal-to-noise ratios (SNRs), an efficient high-rate protograph quasi-cyclic (QC) low-density parity-check (LDPC) codes is optimally constructed. By an optimized protograph template, the code framework is firstly produced by the extensions of the…
Xinyuanmeng Yao, Xiao Ma
This paper first presents a new approach to evaluating the descriptive complexity of finite-length binary sequences. Specifically, we investigate the sequence-wise recovery behavior induced by polar compression and successive cancellation decoding (SCD), and define the polar complexity of a sequence as the minimum…
Jiamin Sun, Zhongjie Zhu, Renwei Tu, Zhibo Xie + 1 more
In Versatile Video Coding (VVC), the partition patterns for coding units (CUs) have significant impact on the encoding efficiency. Determining the optimal CU partition is particularly time-consuming due to the calculation and comparison of rate-distortion costs for all possible partition patterns, especially during the…
Yuchen Liao, Wenyi Zhang
—In downlink massive random access (DMRA), a base station transmits messages to a typically small subset of active users, selected randomly from a massive number of total users. Explicitly encoding the identities of active users would incur a significant overhead scaling logarithmically with the number of total users.…
Alice Tor, Yuxin Wu, Stephen E Clarke, Lisa Yamada + 2 more
The complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying…
Julia Lieb, Michael Schaller
The construction of Maximum Distance Profile (MDP) convolutional codes in general requires the use of very large finite fields. In contrast convolutional codes with optimal column distances maximize the column distances for a given arbitrary finite field. In this paper, we present a construction of such convolutional…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Hillel J. Chiel, Jay S. Coggan, Gourav Datta, Jean-Marc Fellous + 3 more
Since the advent of widely accessible AI tools, AI technology has been in high demand by businesses, academic researchers and individuals. Technology companies are building AI infrastructure at a rapid pace, and these facilities consume vast and growing resources, particularly electricity and water, with significant…
Yuto Tanaka, Issei Sato
Recent work on large language models (LLMs) has emphasized the importance of scaling inference compute. From this perspective, the state-of-the-art method Scattered Forest Search (SFS) has been proposed, employing Monte Carlo Tree Search with carefully crafted initial seeds and textual optimization for multi-turn code…
Authors not listed
The Hidden Subgroup Problem (HSP) unifies several landmark quantum algorithms, yet systematic exploration of its variants and modern applications has slowed. This paper revives HSP-based algorithm design by examining new group structures with direct relevance to post-quantum cryptography, lattice problems, and…