arXiv subjects

Harish Ganapathy

Publications and source records attributed to Harish Ganapathy.

5 recordsLinked to original sources

arXiv2025

Gemini: A Family of Highly Capable Multimodal Models

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

cs.CL↗

arXiv2024

Controlled Decoding from Language Models

KL-regularized reinforcement learning (RL) is a popular alignment framework to control the language model responses towards high reward outcomes. We pose a tokenwise RL objective and propose a modular solver for it, called controlled decoding (CD). CD exerts control through a separate prefix scorer module, which is trained to learn a value function for the reward. The prefix scorer is used at inference time to control the generation from a frozen base model, provably sampling from a solution to the RL objective. We empirically demonstrate that CD is effective as a control mechanism on popular benchmarks. We also show that prefix scorers for multiple rewards may be combined at inference time, effectively solving a multi-objective RL problem with no additional training. We show that the benefits of applying CD transfer to an unseen base model with no further tuning as well. Finally, we show that CD can be applied in a blockwise decoding fashion at inference-time, essentially bridging the gap between the popular best-of-K strategy and tokenwise control through reinforcement learning. This makes CD a promising approach for alignment of language models.

cs.LG↗

arXiv2012

Feedback Allocation For OFDMA Systems With Slow Frequency-domain Scheduling

We study the problem of allocating limited feedback resources across multiple users in an orthogonal-frequency-division-multiple-access downlink system with slow frequency-domain scheduling. Many flavors of slow frequency-domain scheduling (e.g., persistent scheduling, semi-persistent scheduling), that adapt user-sub-band assignments on a slower time-scale, are being considered in standards such as 3GPP Long-Term Evolution. In this paper, we develop a feedback allocation algorithm that operates in conjunction with any arbitrary slow frequency-domain scheduler with the goal of improving the throughput of the system. Given a user-sub-band assignment chosen by the scheduler, the feedback allocation algorithm involves solving a weighted sum-rate maximization at each (slow) scheduling instant. We first develop an optimal dynamic-programming-based algorithm to solve the feedback allocation problem with pseudo-polynomial complexity in the number of users and in the total feedback bit budget. We then propose two approximation algorithms with complexity further reduced, for scenarios where the problem exhibits additional structure.

cs.IT↗

arXiv2012

Exploiting Sparse Dynamics For Bandwidth Reduction In Cooperative Sensing Systems

Recently, there has been a significant interest in developing cooperative sensing systems for certain types of wireless applications. In such systems, a group of sensing nodes periodically collect measurements about the signals being observed in the given geographical region and transmit these measurements to a central node, which in turn processes this information to recover the signals. For example, in cognitive radio networks, the signals of interest are those generated by the primary transmitters and the sensing nodes are the secondary users. In such networks, it is critically important to be able to reliably determine the presence or absence of primary transmitters in order to avoid causing interference. The standard approach to transmit these measurements from sensor the nodes to the fusion center has been to use orthogonal channels. Such an approach quickly places a burden on the control-channel-capacity of the network that would scale linearly in the number of cooperating sensing nodes. In this paper, we show that as long as one condition is satisfied: the dynamics of the observed signals are sparse, i.e., the observed signals do not change their values very rapidly in relation to the time-scale at which the measurements are collected, we can significantly reduce the control bandwidth of the system while achieving the full (linear) bandwidth performance.

cs.IT↗

arXiv2011

Sub-modularity and Antenna Selection in MIMO systems

In this paper, we show that the optimal receive antenna subset selection problem for maximizing the mutual information in a point-to-point MIMO system is sub-modular. Consequently, a greedy step-wise optimization approach, where at each step an antenna that maximizes the incremental gain is added to the existing antenna subset, is guaranteed to be within a (1 - 1/e) fraction of the global optimal value. For a single antenna equipped source and destination with multiple relays, we show that the relay antenna selection problem to maximize the mutual information is modular, when complete channel state information is available at the relays. As a result a greedy step-wise optimization approach leads to an optimal solution for the relay antenna selection problem with linear complexity in comparison to the brute force search that incurs exponential complexity.

cs.IT↗