title: Hierarchical State Space Models for Continuous Sequence-to-Sequence
    Modeling
summary: Reasoning from sequences of raw sensory data is a ubiquitous problem across
  fields ranging from medical devices to robotics. These problems often involve
  using long sequences of raw sensor data (e.g. magnetometers, piezoresistors) to
  predict sequences of desirable physical quantities (e.g. force, inertial
  measurements). While classical approaches are powerful for locally-linear
  prediction problems, they often fall short when using real-world sensors. These
  sensors are typically non-linear, are affected by extraneous variables (e.g.
  vibration), and exhibit data-dependent drift. For many problems, the prediction
  task is exacerbated by small labeled datasets since obtaining ground-truth
  labels requires expensive equipment. In this work, we present Hierarchical
  State-Space Models (HiSS), a conceptually simple, new technique for continuous
  sequential prediction. HiSS stacks structured state-space models on top of each
  other to create a temporal hierarchy. Across six real-world sensor datasets,
  from tactile-based state prediction to accelerometer-based inertial
  measurement, HiSS outperforms state-of-the-art sequence models such as causal
  Transformers, LSTMs, S4, and Mamba by at least 23% on MSE. Our experiments
  further indicate that HiSS demonstrates efficient scaling to smaller datasets
  and is compatible with existing data-filtering techniques. Code, datasets and
  videos can be found on https://hiss-csp.github.io.
paper_authors._id: 65cef0dfe5413a96b0884bc9
paper_authors.name: Raunaq Bhirangi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-16T16:20:20.506Z
paper_authors._id: 65cf8a3f00d099dc4cbbb653
paper_authors.avatarUrl: /avatars/9c0ff791e679000aa7f446d8f5f0c3e6.svg
paper_authors.fullname: Raunaq Bhirangi
paper_authors.type: user
paper_authors.user: raunaqbhirangi
paper_authors._id: 65cef0dfe5413a96b0884bca
paper_authors.name: Chenyu Wang
paper_authors._id: 65cef0dfe5413a96b0884bcb
paper_authors.name: Venkatesh Pattabiraman
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-19T09:34:24.821Z
paper_authors._id: 65d12297617d1f7450e7dd06
paper_authors.avatarUrl: /avatars/7d02f152fdeed4d5bdebe120f9864794.svg
paper_authors.fullname: Venkatesh Pattabiraman
paper_authors.type: user
paper_authors.user: venky2k
paper_authors._id: 65cef0dfe5413a96b0884bcc
paper_authors.name: Carmel Majidi
paper_authors._id: 65cef0dfe5413a96b0884bcd
paper_authors.name: Abhinav Gupta
paper_authors._id: 65cef0dfe5413a96b0884bce
paper_authors.name: Tess Hellebrekers
paper_authors._id: 65cef0dfe5413a96b0884bcf
paper_authors.name: Lerrel Pinto
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T14:32:26.515Z
paper_authors._id: 6388cdb6685efaedd15dc0f3
paper_authors.avatarUrl: /avatars/b2b74454bf86bf61fd90ba088644f547.svg
paper_authors.fullname: Lerrel Pinto
paper_authors.type: user
paper_authors.user: lerrel
paper_title: Hierarchical State Space Models for Continuous Sequence-to-Sequence
    Modeling
paper_summary: Reasoning from sequences of raw sensory data is a ubiquitous problem across
  fields ranging from medical devices to robotics. These problems often involve
  using long sequences of raw sensor data (e.g. magnetometers, piezoresistors) to
  predict sequences of desirable physical quantities (e.g. force, inertial
  measurements). While classical approaches are powerful for locally-linear
  prediction problems, they often fall short when using real-world sensors. These
  sensors are typically non-linear, are affected by extraneous variables (e.g.
  vibration), and exhibit data-dependent drift. For many problems, the prediction
  task is exacerbated by small labeled datasets since obtaining ground-truth
  labels requires expensive equipment. In this work, we present Hierarchical
  State-Space Models (HiSS), a conceptually simple, new technique for continuous
  sequential prediction. HiSS stacks structured state-space models on top of each
  other to create a temporal hierarchy. Across six real-world sensor datasets,
  from tactile-based state prediction to accelerometer-based inertial
  measurement, HiSS outperforms state-of-the-art sequence models such as causal
  Transformers, LSTMs, S4, and Mamba by at least 23% on MSE. Our experiments
  further indicate that HiSS demonstrates efficient scaling to smaller datasets
  and is compatible with existing data-filtering techniques. Code, datasets and
  videos can be found on https://hiss-csp.github.io.
paper_ai_summary: Hierarchical State-Space Models (HiSS) stack structured state-space models to create a temporal hierarchy, outperforming sequence models like causal Transformers, LSTMs, S4, and Mamba in continuous sequential prediction across real-world sensor datasets.
paper_ai_keywords: Hierarchical State-Space Models HiSS state-space models causal Transformers LSTMs S4 Mamba MSE