Nate Kushman

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Many studies show that, when Internet links go up or down, the dynamics of BGP may cause several minutes of packet loss. The loss occurs even when multiple paths between the sender and receiver domains exist, and is unwarranted given the high connectivity of the Internet. Our objective is to ensure that Internet domains stay connected as long as the(More)
Wideband technologies in the unlicensed spectrum can satisfy the ever-increasing demands for wireless bandwidth created by emerging rich media applications. The key challenge for such systems, however, is to allow narrowband technologies that share these bands (say, 802.11 a/b/g/n, Zigbee) to achieve their normal performance, without compromising the(More)
We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a system of linear equations, while simultaneously recovering an alignment of the variables and numbers in these equations to the problem text. The learning algorithm uses varied supervision, including(More)
This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant variables and their values. ARIS then maps this information into an equation that represents the problem, and enables its (trivial) solution as shown in Figure 1. The(More)
Current wireless protocols retransmit any packet that fails the checksum test, even when most of the bits are correctly received. Prior work has recognized this inefficiency, however the proposed solutions (e.g., PPR, HARQ and SOFT) require changes to the hardware and physical layer, and hence are not usable in today's WLANs and mesh networks. They are(More)
Industry observers expect VoIP to eventually replace most of the existing land-line telephone connections. Currently however, quality and reliability concerns largely limit VoIP usage to either personal calls on cross-domain services such as Skype and Vonage, or to single-domain services such as trunking, where a core ISP carries long-distance voice as VoIP(More)
We consider the problem of translating natural language text queries into regular expressions which represent their meaning. The mismatch in the level of abstraction between the natural language representation and the regular expression representation make this a novel and challenging problem. However, a given regular expression can be written in many(More)
We study machine learning formulations of inductive program synthesis; that is, given input-output examples, we would like to synthesize source code that maps inputs to corresponding outputs. Our aims in this work are to develop new machine learning approaches to the problem based on neural networks and graphical models, and to understand the capabilities(More)
This paper explores the task of translating natural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does not utilize domain-specific crafting, learning to translate directly from a parallel corpus. To fully explore the potential of neural models, we propose a methodology for(More)
Recent work across several AI subdisciplines has focused on automatically solving math word problems. In this paper we introduce MAWPS, an online repository of Math Word Problems, to provide a unified testbed to evaluate different algorithms. MAWPS allows for the automatic construction of datasets with particular characteristics, providing tools for tuning(More)